І, І І І І І І І І - І І І : є 2015

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Transcription:

004. 519. 217 І, І І І І І І І І - І І І 05. 13. 06 : є 2015

2. 6.. 7 1. -... 13 1.1. 13 1.2.. 21 1.3.... 25 1.3.1. 26 1.3.2.. 27 1.3.3... 29 1.3.4.. 29 1.3.5. 30 1.4.. 32 1.4.1.... 35 1.4.2. 38 1.5... 39 1.5.1.... 39 1.5.2. TRА... 40 1.5.3.... 41 1.6.. 42 1.6.1... 43 1.6.2.. 44 1.6.3. є... 44 1.7.. 45 1.7.1.... 45 1.7.2. 46 1... 47

2. 49 2.1... 49 2.1.1. 51 2.2. 54 2.2.1. 54 2.2.2... 56 2.3... 58 2.3.1.. 58 2.3.2... 60 2.3.3... 62 2.3.4.. 64 2.3.5. 66 2.3.6. 70 2.3.7.. 72 2.3.8.. 74 2.3.9. 77 2.4.. 79 2.4.1. - є. 80 2.4.2. є - є.. 84 2.5.. 88 2.5.1.. 89 2.5.2... 92 2.5.3.... 92 3

2. 93 3... 95 3.1... 96 3.2.. 99 3.3.. 103 3.3.1.... 104 3.3.2. 105 3.3.3... 107 3.4... 108 3.5. 110 3.5.1. 111 3.5.2... 111 3.5.3...... 112 3.5.4.. 119 3.6.... 125 3. 125 4.. 127 4.1... 128 4.1.1.. 128 4.1.2. 129 4.1.3.. 132 4.1.4.. 132 4.1.5... 138 4.1.6. 141 4.2. 141 4.2.1... 146 4.2.2. 147 4

4.2.3. 148 4.2.4.. 150 4.3. 151 4.4. 152 4.5. 156 4... 157. 159 162.... 177. 185... 191.. 201 5

6 - є FP Function point ICAM Integrated Computer-Aided Manufacturing IDEF Icam DEFinition IDEF0 Function Modeling IEEE Institute of Electrical and Electronics Engineers IEC International Electro technical Commission ISO International Organization for Standardization PROMISE PRedictOr Models In Software Engineering CASE Computer-Aided Software Engineering CMM Capability Maturity Model

ь ь. - ( ), є є,, -,. -, є.. -,., є.., TRW, Hewlett-Packard IBM, 4-100,. є,,,,,., є. ISO/IEC 25010-2011,, є. ISO/IEC 9126,,.,,. є є, 7

,. є, є,,,,,. є,. є. -,. -, є.,, є ь, є. ',,. 2009-2015, 1066/609 26.11.2009, -,,. -,, є,,.. є -. : 8

-, є, ; -,, - ; - ; -, є - -. є -., -...,. -. -.. є є. 9

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ь є., є, : є ( 20%, ); ;, є. є.,, 6,5%,. 8%. є 7% 8,8%. - є,.,,, 11

( «є», )..,,,, є. Д1, 5, 6, 10, 11 13, 17, 20Ж.,, Д9, 14-16, 18, 19Ж, Д12Ж, Д2Ж, Д3Ж, [7],, Д4,8Ж. ь. : - «, DESSERT» (2010,, 2012-2013,, 2014, ); (2011-2012, ); «TAAPSD» (2010,, 2011, ); -... (2013, 2015, ); (2014, ).. 20, 12, 2, 6. 12

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,. [32]. [33],. є,, є. -.. - ( ),, є.,,, [34, 35, 36, 37, 38]., - є,,. [34],.,,,. [35]. є,,. є, [36], є є., [36], є. -, є є. (, 14

), є.,., є, ( «1»), ( ), є. [37],, ь,,,... є [21, 38] є, є., ь,..,. [21, 38]. ь, є, є. є ( 2015 [21, 39]),, є ( є 2015 ). [38, 33, 34]. 95% ( ), є,, є,, 15

16, є [21, 38]. ь є ь, є, [38, 39, 40], є,..., TRW, Hewlett-Packard IBM, 4-100,. є,,,,,., є.,. є є. є. ь (Reliability) ISO/IEC 9126 [41, 42, 43], є ISO/IEC 25010:2011 Д44Ж є є, : Q f ( F, E, C, U, R, S, P, M ) (1.1) F - (Functional suitability), E - (Performance efficiency), C - (Compatibility), U - (Usability), R - (Reliability), S - (Security), P - (Portability), M - (Maintainability). [45,46] [47-51]. ISO/IEC 9126,

,,. (defect, fault) [51,53], є,,,,.. [54, 55]. [41, 42, 43, 43] : N est ; defects) (the Estimated Density of not revealed N Nrev ( t) ED( t) (1.2) Size ( t) est Nrev -, Size soft - (,,, ); revealing) soft (the Estimated Resolution of defects N ( t) ER t Nest є 17 rev ( ) (1.3), [56-60]. [56, 58], 0,8. 0,95. [61],, є CMM (Capability Maturity Model),. 1.1. є,

є 5 15%. є (,, ),,. 1.1 -, % 1 0,85 15 0,75 2 0,89 11 0,44 3 0,91 9 0,27 4 0,93 7 0,14 5 0,99 5 0,05 18.,,. [55Ж є ( ),,,. є. є ь.,, :,,. є. є, є ( )

,., є є..,,..,,, [56]. [56, 57, 58, 59], є :, є, є 5 % ;, є,,, є 15 % ;, є, є 30 % ;, є,, є 50 80% ;. [55], (software testing),,.. [60],, [61],, [59]., [60-64], є, 80% є. [61] є, 25 %.,, 19

є,,., $50. [28, 57]. є, є, є, є,.. [60, 61, 64], є є. [57, 58, 59], є,. [61], є.,., (,,, ) є., є, (1.2) (1.3),. є [64]. [57-63],.,., [59], 80% 20 %, 50% 5 %. є 20

. є. [59], є є. є. є,,. 1.2. 21 ь І -.1.1 -,. є,. 1.2. ( ), є, [55].,

[51],,. 1.2. 22 1 2 3 - - - 4 5.. 6 - - - - 7.1.2-1,.. [62] є.,

. 2, є,,. 3,.. [63] є :,,,,. [64, 65] є,.... [66] є.,,.., є.,. 6,.. [67], є,,., є,,,.....,. 23

. є,,.. [1,6,13,2], - [69], - [70,71], [71], [72], [73], [74], - [75], [76], S- [77], S- [77], [67], [68-79], [1-6, 13-15]. Д6,13Ж,,,... 1,.. 2,.. 3. [2],,,,,,, S-, S-,,.,.. 4, є. 5. [5], - S.6.. 7,. 8,. 9. є. 10.. Д3, 4Ж, Д78, 79],..11. 24

Д1-6, 13-15],.. є [21]., [68,78,79],. є ( ),,. є є.,.. 1.2.. 25 1.3., [49,68,80-89],,. [51]. [49,68,79],,,., [80-85],., [49,50],., [86-89],., [48, 50, 51],..

1.3.1. - є ь, [49,68], є t 2 Nest E 1 exp( K t ) (1.4) -, є, t - (, t= 4 є ), -. є,. ь ь, [49,68], є ED N est ED A D ( SA ST SQ) ( SL SS SM SU SX SR) (1.5) N est ED LOC (1.6) LOC (lines of code). (1.5) :, (,, ), D,. : SA ( ), ST, SQ -, SL - (, ), SS -, SM -, SU -, SX, SR.,,. ь [79] є N est 26 N K (1.7) total

Ntotal -, є, K - є.,, є. 27 1.3.2. є. є (FP-function point), IBM (AХХКЧ AХЛrОМСЭ) 1979, [80]., International Function Point Users Group [81], є FP : (EбЭОrЧКХ ТЧЩЮЭЬ) ; (External outputs) ; є є (External inquiries); (Internal logical files); є (External interface files). : 1 -, 2 -, 3. (, ) FP ( 3 FP 15). FP (UFP) є 3 UFP (1.8) 5 i 1 j 1 N ij W ij Nij Wij i j. UFP VAF ( 0.62 VAF 1. 35), є (,, ) AFP ( UFP CFP) VAF (1.9)

28 CFP FP,,,, /. є FP (1.10) Nest AFP N1 FP N 1 - FP, [82],. 1.2. 1.2 - FP FP 1 1,25 1,75 0,4 0,6 5 UFP, AFP, [83] UFP,,. ISO/IEC 20926:2003 [84] є UFP.,., [85],. 100%..

1.3.3., [49, 50], IBM,,.. є. i -,. i+ 1 - є, :,,,. N est є i = 0,9 i + 0,15 І i (1.11) i = 0,15 i + 0,006 І i (1.12) N est = 23 i + 2 i (1.13) i -, i -, І i -, i -. є IBM... є, є є. є. є,,, (,,,, ). 29 1.3.4., [86], є є N est

N est 4.86 0. 018 LOC (1.14), [87], є N N n, 4.2 n 0,0015 ( ) (1.15) est LOC i i 1 i n 4/3 LOC i -., [88], є est 30 N est 2 LOC (1.16) N est 0.069 0.0156 0,00000047 ( LOC ), [89], є є A 0, A1, A2, N est 2 L A0 A1 ln LOC A2 ln LOC (1.17), є : A 0 0.0012 ; A1 0.0001; A2 0.000002 1.3.5., - PROMISE (PRedictOr Models In Software Engineering) [90]. є : 50 500, 200 1000, 100 700,.,, 1.3. 1.3,,,

, (80- ), є., є A 0, A1, A2,, C, C++, Java, PHP, Perl, JavaScript.,, є., є. [91], «., r -,,,,». 31 1.3-1 231 55538 341 1 005 4 149 10 114 2 882 411737 618 7 416 49 651 143 909 3 379 119701 496 2 159 10 441 25 408 4 438 129307 475 2 332 11 648 28 031 5 351 113247 184 2 043 9 693 23 694 6 645 208654 338 3 761 21 273 53 012 7 608 66302 522 1 198 6 579 12 409 8 856 287317 114 5 177 32 034 83 621 9 964 113026 472 2 039 12 246 23 636 10 944 112926 394 2 038 12 152 23 610 є 2 917 16 987 42 744 є [47, 48, 56, 58], є.

,,,,.,.,. [29, 47, 48, 51, 56, 58, 59, 60],,. 32 1.4. Д92Ж,,,., є (,,, ), є. Д93Ж, є, є. ґ,, є [94]. є., [59, 62, 95, 96] є є.. Д95Ж є : «-,.. :,».. [62] є, «,.,,,.,

,,». є :,,,.. [96] є, «,».. [59] є, «, 15». [97, 98], є.,,. є. [99],, є є ( ), є,,.. [100] є,... [101] є,.,. [102] є, є,,. є,., є, є 33

,,,,,. Д103], є,.,. [104],, є,,. є, є.,,. [105] є, є. є є,, є -., є., є є,,,. є,,,,, є.., [106]. -, є. є, є... - [107]. -, ь, є,,. 34

(,,, ) є, є. [108, 109, 110] ь, ь. є, є є (,,,,, ) є, є,. є є є [108]. [109] є,.. [51], є,,.,,,. IEEE 1061[111], є. 35 1.4.1. Д42,55,111Ж (metric) ( ),. ( ) -.,. є.,

(,,,,, ),.,,,,.,,,.. є - є є є. TIOBE Software Д112], Google, Google Blogs, Yahoo!, Wikipedia, MSN, YouTube, Bing, Amazon, Baidu...12., є є є. є є є.., [113Ж. n n 1 n2, n1-, n2 - ; N N 1 N2, N1-, N 2 -. - є [107]. Ч є [114, 115], є, C Ch P 2M 3C 0. 5T (1.18) 36

P -, M -, C -, T -. є [114, 115] Ncall -, N mod ul C N 37 call Jilb (1.19) Nmodul -. є є. [116],,,, є, є є. є : WMC (Weighted methods per class) WMC C i, i=1 n, n -, Ci - i -. DIT (Depth of Inheritance tree) є є - -. NOC (Number of children) є -. CBO (Coupling between object classes) є,,. RFC (Response for a class) є ( ), є. LCOM (Lack of cohesion in Methods) є,. Vi - M i, Vj - M, V V 0 j i j M, M, V V 0. є i j i j LCOM є : NR0 NR1, NR0 NR1 LCOM (1.20) 0, NR0 NR1 NR0 -, NR1 -. [7, 8, 9, 10, 16, 17].,.1.6.,.

ґ.,. є є.,.,,,. 38 1.4.2. Ndepend, [117, 118] є 80, NET Framework, 19, 22, 18, 12.. Resource Standard Metrics (RSM) [119] є 20,,, C, C++, C# Java,,,. Eclipse Metrics Plugin [120] є 20 Java,. IBM Rational Telelogic Logiscope [121]. CSV, HTML, XML.. PVCS Version Manager [122], Microsoft Visual Source Safe [123], SoftBench CM, SoftStatic, Development Manager Hewlett-Packard [124], RCS, CVS, Subversion (SVN), Mercurial, Git, Bazaar.

,,.. є.,,.,, є.,... 39 1.5.,. 1.5.1., [113], є N est V / 3000, V N log 2 n (1.21) V - є, n -, N -. [7].,,,. 1.4.

1.4-40 1 2 3 4 5 6 7 N 1 605 828 11 300 8 208 14 563 21 670 25 013 n 80 96 120 115 123 132 148 1 N 641 741 7853 5315 9354 13466 17016 2 n 2 180 92 1388 513 1628 1956 2128 n 260 188 1508 628 1751 2088 2276 N 1 246 1 569 19 153 13 523 23 917 35 136 42 029 V 9 996 11 853 202 225 125 691 257 681 387 477 468 719,, % 4 6 36 39 57 76 89 3,33 3,95 67,41 41,90 85,89 129,16 156,24-17% -34% 87% 7% 51% 70% 76% 87%, є 49%. є : 1),, є,, є ; 2) є, є., є. 1.5.2. TRА TRW [48] Nest Ltot k1 0.1 Cinf k2 0.2 Cc k3 0.4 Cio k4 ( 0.1) U read k5 (1.22) Ltot -, ; C inf - є ; Cc -, ; - ; U read Cio - -,

є є ; ki - є i -, TRW. [8]., є 44%, є є 31%., є, TRW, є,, 0.63 k 1 0. 91, 0.43 k 2 0. 76. є є,. є, є. 41 1.5.3. [125] N est 0,042 MCI 0,075 N 0, 00001 HE (1.23) MCI, N, HE [113], N est 0,25 MCI 0,53 DI 0, 09 VG (1.24) DI, VG - - [107]. [126]., є, є

. є, [8] є є є.,, З.,. 31-49%.,. є є. є,,. 42 1.6., [49, 58, 64], є є., [47, 56, 60, 126Ж,.. є., є.

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1.6.2. [129-135Ж. є :, є. є [135]. [136,137Ж. є,,,,,.,, є,... [138], є MatLab.,,, є. 44 1.6.3. є є [139, 140].,,,. є,.., є.

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51 ь M 1 ь 1. ь,,,,,,, M 2 2.,,, M 3 M 4 3. є 4.,,,,.,,, є.2.1 - є ґ,. 2.1.1. [16],,. 2.1,. 2.2.

52 2.1-1. 2. - 3. - 4. є є, 1. - 2. 3. 4. -. 5. 6. 7. 8. 9. - 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. ( ) 25. - 26. 27. 28. є - є 29. WMC 30. DIT 31. NOC 32. CBO 33. RFC 34. LCOM 35. NOO 36. NOA 37. SI 38. A 39. CE 40. I

53 -,,, - є є 1. 2. 3. є 4.,,.,, є,,,, -, s,,,,,,,,,,,,,wmc, LCOM, NOO,NOA,SI,, CBO, RFC, CA, CE, I DIT, NOC -. 2.2 - є ґ. є. є є.

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., є,,. є є. (Modified Metric) : MM n n n ( 2 i 1) Ncom _ i (2 i i! 2) Nnotcom _ i ( i i!) N func _i i 1 i 1 i 1 56 (2.1) ( i 1... n), ( 2 i 1) є -, N com _ i -, ( 2 i i! 2) є -, N notcom _ i -, ( i i! ) є -, N func_ i -.,. 2.2.2.,,,, є : 1), ; 2) ; 3) ; 4) (2.1).,. 2.3.

57? N 1 -?. N 2 _ com -?. N _ notcom 2 -?. N i _ com -? N _ i notcom Є? (2.2) 2.3 -

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є, є, є ;.,,, ;.,, ;., Case -.,,,,,. є.. є.,,. є,,. : є є, M M, M,..., M }; { 1 2 n 59

60 M є X x, x,..., x }, { 1 2 n ( ); ;, (,,,,, ),,. 2.3.2. є Nest x... ґ 1 M1 x2 M 2 xn M n (2.2),,,,., ( є,,, ) 0. 05. є [143]: x1 M x1 M x1 M 11 21 n1 x 2 x 2 x 2 M M M 12 22 n2... x n... x... n... x n M M 1n M 2n nn N d1 N N d 2 dn (2.3) d N d... N dn N 1, 2,,, M, i j

61 i -, j -. є. є,,. - [144],,.,. є є (2.3), є. є,. є є є, є є.,,. 2.4. є - є. WMC C Mac NPM DIT NOC LCOM CBO RFC CA LOC - CE 2.4 - є є є ',.

2.3.3. ' : є, є R, 0. 7. є D M i M i D j R,, [90]. є..1,. 2.. 2.3. 2.3 - / є.. 1 RFC 0,8650 0,0114 2 WMC 0,8285 0,0169 3 LCOM 0,8165 0,0244 4 LOC 0,8151 0,0204 5 NPM 0,7935 0,0304 6 CE 0,7381 0,0186 7 CBO 0,6994 0,0340 8 CA 0,6177 0,0401 9 MOA 0,5920 0,0173 10 AMC 0,4710 0,0143 11 MAX_CC 0,4598 0,0136 12 CBM 0,3479 0,0119 13 AVG_CC 0,3126 0,0095 14 DAM 0,3087 0,0035 15 IC 0,2770 0,0097 16 NOC 0,2300 0,0064 17 DIT 0,1317 0,0048 18 MFA 0,0816 0,0036 19 LCOM3 0,0251 0,0049 20 CAM -0,1581 0,0029 62

є, M RFC, WMC, LOC, CE, NPM, LCOM, CBO R D, M.. 2.3 i.. 1. RFC є. H R 0; H : R 0 0 a ; (2.4) 0 : 0. [145]. є z=1.293, z- 0. 1525. z 1 46 3 z t- t fact 8. 4787. t- 0. 01 k= 40 t 3. 55 z 0.01 63. t fact t 0.01 (8,4787>3.55), є, є. : WMC, LOC, CE, NPM, LCOM, CBO. 0. 01. 2.. 2.3 R,.. 2.4. D M i, 0.6994 R, 0. 8650, 0.6018 R, 0. 9215. Д145], ' D M i є, R X, Y 0. 7., 0. 01 D M i

M WMC, RFC, LCOM, NPM, CBO, CF, CE,.. 64 2.4 - R, D M i R, D M i R, D M i RFC 0,8650±0,0159 0,8085 0,9215 WMC 0,8285±0,0194 0,7597 0,8973 LOC 0,8165±0,233 0,7338 0,8992 CE 0,8151±0,213 0,7395 0,8907 NPM 0,7935±0,0260 0,7012 0,8858 LCOM 0,7381±0,0203 0,6659 0,8103 CBO 0,6994±0,0275 0,6018 0,7970 є... 2.3.4. WMC. NPM., WMC.,, є.. LCЇM є,, NPM. RFC є,,.,

., RFC, WMC NPM. є, є,. CBO є,,. RFC є., RFC є CBO,, є. CA CE CBO. CBO є CA CE,. є є. NOC є є, є є. CA, є є. DIT є, є є. DIT,, є.. 2.5. 65 WMC LOC LCOM NPM DIT RFC NOC CE CBO CA 2.5 - є - є

: 1)WMC-RFC, 2)WMC-LCOM, 3)WMC-NPM, 4)CBO-CA, 5)RFC-LCOM, 6)RFC-CE, 7)RFC-NPM, 8)LCOM-NPM.,,.. 66 2.3.5. є - є, - PROMISE (PRedictOr Models In Software Engineering) [90].. 2.5., :, ( 1000 500 000 ), ( 50 13 000)... 2.5 - / 1 3 http://code.google.com/p/promisedata/wiki/log4j 2 3 http://code.google.com/p/promisedata/wiki/synapse 3 4 http://code.google.com/p/promisedata/wiki/lucene 4 4 http://code.google.com/p/promisedata/wiki/poi 5 5 http://code.google.com/p/promisedata/wiki/ant 6 4 http://code.google.com/p/promisedata/wiki/camel 7 4 http://code.google.com/p/promisedata/wiki/xerces 8 4 http://code.google.com/p/promisedata/wiki/xalan 9 3 http://code.google.com/p/promisedata/wiki/forrest 10 4 http://code.google.com/p/promisedata/wiki/ivy 11 5 http://code.google.com/p/promisedata/wiki/jedit 12 3 http://code.google.com/p/promisedata/wiki/velocity 46

,, : 1) є 67 R M i M j ; 2) R M i M j ; 3) R M i M j. 1. WMC RFC є R, WMC RFC H R 0; H : R 0 0 a ; (2.6) 0 : 0 R,. [145]. WMC RFC є 1 z=1.333, z- z 0. 072. 195 3 z t- t fact 18. 514. t- 0. 01 Ф= t 3. 29 z 0.01. t fact t 0.01, є, є, 0. 01.,. 2.3.4: WMC-LCOM, WMC-NPM, CBO-CA, RFC-LCOM, RFC-CE, RFC-NPM, LCOM-NPM. 0.01. 2. є ~ R M i, M j ~ 2 R (. 2.6) (. 2.7).

68 2.6 - є WMC DIT NOC CBO RFC LCOM CA CE NPM WMC 1-0,1311 0,1099 0,5038 0,8988 0,7051 0,2596 0,4594 0,7883 DIT -0,1311 1-0,0547-0,0704-0,0605-0,0671-0,0801 0,1117-0,0777 NOC 0,1099-0,0547 1 0,3527 0,0668 0,0800 0,3129 0,0652 0,0916 CBO 0,5038-0,0704 0,3527 1 0,5102 0,3731 0,7384 0,5354 0,4065 RFC 0,8988-0,0605 0,0668 0,5102 1 0,6161 0,1983 0,6025 0,6688 LCOM 0,7051-0,0671 0,0800 0,3731 0,6161 1 0,2800 0,4798 0,7466 CA 0,2596-0,0801 0,3129 0,7384 0,1983 0,2800 1 0,1344 0,3280 CE 0,4594 0,1117 0,0652 0,5354 0,6025 0,4798 0,1344 1 0,4408 NPM 0,7883-0,0777 0,0916 0,4065 0,6688 0,7466 0,3280 0,4408 1 ~ 2.6 0.6 R 0. 9, M i, M j,. 2.3.4., 2.7 є. 2.7 - є WMC DIT NOC CBO RFC LCOM CA CE NPM WMC 0 0,0026 0,0067 0,0060 0,0016 0,1130 0,0134 0,0935 0,0921 DIT 0,0026 0 0,0111 0,0141 0,0008 0,0028 0,0199 0,0086 0,0083 NOC 0,0067 0,0111 0 0,0030 0,0120 0,0027 0,0311 0,0141 0,0105 CBO 0,0060 0,0141 0,0030 0 0,0097 0,0252 0,1116 0,0429 0,0360 RFC 0,0016 0,0008 0,0120 0,0097 0 0,1110 0,0103 0,1046 0,0706 LCOM 0,1130 0,0028 0,0027 0,0252 0,1110 0 0,0069 0,0271 0,0041 CA 0,0134 0,0199 0,0311 0,1116 0,0103 0,0069 0 0,0172 0,0052 CE 0,0935 0,0086 0,0141 0,0429 0,1046 0,0271 0,0172 0 0,0278 NPM 0,0921 0,0083 0,0105 0,0360 0,0706 0,0041 0,0052 0,0278 0 3. ~ R M i, M j ~ 2 R, S 2 ~ 2 n R n= 46,. n 1 2 S R. n

~ R 0 ~ R M 1, M 2 R. 69 R t 0. 001 R. ~ ~ R. 0 R M 1, M 2. 2.8. R 2.8 - є WMC, RFC 0,8988 0,0091 0,8809 0,9166 WMC, LCOM 0,7051 0,0771 0,5539 0,8563 WMC, NPM 0,7883 0,0696 0,6519 0,9247 CBO, CA 0,7384 0,0766 0,5882 0,8886 RFC, LCOM 0,6161 0,0764 0,4663 0,7659 RFC, CE 0,6025 0,0742 0,4571 0,7479 RFC, NPM 0,6688 0,0610 0,5493 0,7883 LCOM, NPM 0,7466 0,0148 0,7177 0,7756., 0.01. 2.3.4 WMC-LCOM, WMC-NPM, CBO-CA, RFC- LCOM, RFC-CE, RFC-NPM, LCOM-NPM., є є,. є є. є. є :

?. 70 2.3.6., [146], [90]. STATGRAPHICS PLUS [147] :, є, є ;, є ;.,, є,. 2.9. 2.9 -, %, % WMC 83,762 83,762 6,700 CBO 5,724 89,486 0,457 RFC 4,540 94,026 0,363 LCOM 3,469 97,495 0,277 CE 1,202 98,697 0,096 NPM 0,841 99,537 0,067 LOC 0,301 100 0,024 2.9, WMC є 83%.

CBO, RFC, LCOM 14%. 2,3 %. є WMC. CBO, RFC, LCOM є є. CE, NPM, LOC,., є. 97%, є,..,. - є ( =0.05 95%).. 2.10. 2.10 - -, % 1 0,98875 94,18 43,7049 0,0000 2 0,73871 2,59 1,20115 0,0000 3 0,67443 1,80 0,834368 0,0000 4 0,55939 0,98 0,455426 0,0000 5 0,36029 0,32 0,149174 0,5113 6 0,18535 0,08 0,355768 0,9065 7 0,16195 0,06 0,0269353 0,7505 71. 2.10.. -.

, є,,,, є. є,. є,.,. 72 2.3.7. 2.3.3 ' RFC, WMC, LOC, CE, NPM, LCOM, CBO. [145] є : R< 0.19, 0.20<R< 0.29, 0.30<R< 0.49, 0.50<R< 0.69, R> 0.70.,.. 2.3.5 є.,, є.,.. 2.3.6,. (2.2) є,.,

.,.. є : 1. : 1.1. є 73 R N d, M i RFC, WMC, LOC, CE, NPM, LCOM, CBO, 1.2. N d M i R ; M i M j R,. R, 0. 29,, N d M i. ; 1.3. R M i,m 1. R 0. 7, є, є, M i M1. 2. : R N d, ; M i 2.1. 2.2. 2.3. 1 rank i ; 2 rank i sum i R N d, ; M i 1 i 2 i rank = rank + rank. 3. sum rank i., є,, є. MS EбМОХ,, є є.., є (2.2).

(2.2) є є (2.3),. 74 2.3.8. [148], є (2.3) -, x... x x i i (2.7) i - i-., : 1) 0; 2) i 0. 1) 0, (2.3) є,, є., 0, ( ). (2.3) є, M. (2.3),. (2.3) є, M. (2.3), M,.,, 0, 0, є. i n

(2.3) є,. (2.3) ь,. є,. [11],, є (2.3)..3., 320-330 є. є,. є,. ( 10) є 15., є,. 2) i- (2.3) i 0, є,. є 75 i 0 x. є,.., (2.3).,, є.,,. (2.3) i

., ь є (2.3). ( ),. (2.2) є,,.., є,.,.,. M..,., ь є M. (2.2) є. є є, є. є є,., ь є. 76

, (2.2).,.. 77 2.3.9.,. (waterfall model) [149] є, /. ь є M. ь є,. (iteration model) [150], (КРТХО),. ь є M.,., [151], є ( ), : 1) ; 2) ; 3)., є

є M. ь є є. (spiral model) [152] є,. є. ь є є.,., ь ь -, є, -. є є : 1) ( є ); 2). є :, ( є,,, ) 0. 05 ; 78

(. 2.3.3) M ; (. 2.3.6), M, ; (. 2.3.6), M ; (. 2.3.4), є M, є є є ; M (. 2.3.5). є.,. 79 2.4. [8, 9]. 1.4.1., є є, є. є є є. 2.4.1. - є - є,, [8]. є

50, 100.. 2.1.1. 2.3.7 -,. : 1. 80-,,, [49, 50, 59, 107, 113-116]; 2. ; 3. є, є., (. 2.2.1) є (. 2.2.2)... 4., є k def 80. k def,,,,,,. є, є k def, 0 k def 1 LOCdef kdef (2.8) LOC total

LOC new - ( ), k def LOC total -., є є, є. k def, (2.3), M, (2.2). M,,,. 2.3.3., d, є N pred _ def Nopen _ def d 100% (2.9) N open _ def N pred _ def -, open def 81 N _ - 1. -, 1, 2, (2.3). M 0.037666, 0. 0111562 3,4,5,6,7. є -15,24%, 0,006. 2. -, 1, 2,. M 0.041791, 0. 197015 3,4,5,6,7. є +12,06%, 0,002.

82 3. -, 1, 2,. M 0.039181, 0. 010729 3,4,5,6,7. є - 16,31%, 0,006. 4. -,, 1, 2, 3,. M 0.040903,0.002491, 0. 154574 4,5,6,7. є +7,71%., 2,15. 5. -,, 4,5,6,. M 0.045252, 0.000418, 0. 143446 1,2,3,7. є -4,23%, 27,57. 6. -,,,.2.2, 1,2,3,. M 0.036212,0.022662, 0. 010300 4,5,6,7. є -7,3%, 0,005. 7. -,,, 1,2,3, 4,. M 0.037597,0.016707,0.045636, 0. 007259 5,6,7. є - 3,82%, 28,80. 8. -,,, 4,5,6,7,.

83 M 0.045499,0.000518,0.143805, 0. 000043 1,2,3. є -5,67%, 30,25. 9.,,, 2,3,4,5,. M 0.040918,0.010483,0.099763, 0. 006205 1,6,7. є -1,40%, 12,69. 36, є.. 2.11 2.11-1-7 є % -15,24 0,0060 1,2,3 12,06 0,0020 2-16,31 0,0060 7,71 2,1500 3,4,5-4,23 27,5700 3-7,30 0,0050-3,82 28,8000 6,7,8-5,67 30,2500 4-1,40 12,6900. 16,31 є 8,19. 1,40 27,3586.. 5,2305, є

16,31% є 8,19%, 1,40%..2.11,, є.. 84 2.4.2. є - є, [9], є - є, - PROMISE (PRedictOr Models In Software Engineering) [90].. 2.5. є 1000 500 000., 7000., [9], (. 2.4.1),,.. 2.3.3,. 2.3.7,,. 1. LCOM, WMC 8, 9 (.. 5 ), (2.3). M 0.0761, 0. 1617, 10. є 12,86%. 2. LCOM, LOC 8, 9 (.. 5 ),. M 0.0652, 0. 0053, 10. є 4,05%. 3. LCOM, RFC 8, 9 (.. 5 ),.

85 M 0.0556, 0. 0307, 10. є -8,39%. 4. LOC, WMC 11, 12 (.. 6 ),. M 0.0136, 0. 2930, 13. є 8,17%. 5. LOC, CBO 11, 12 (.. 6 ),. M 0.0193, 0. 2584, 13. є 30,15%. 6. LOC, RFC 11, 12 (.. 6 ),. M 0.0217, 0. 1290, 13. є 28,33%. 7. RFC, WMC 14, 15 (.. 7 ),. M 0.5973,1. 6413, 16. є 8,31%. 8. RFC, CBO 14, 15 (.. 7 ),. M 0.3935,1. 0736, 16. є -12,36%. 9. RFC, CE 14, 15 (.. 7 ),. M 0.7000,3. 7629, 16. є 42,05%. 10. LOC, WMC 17, 18 (.. 8 ),

. M 86 0.0040,0. 1780, 19. є 8,61%. 11. LOC, WMC 17, 18 (.. 7 ),. M 0.0091,0. 0385, 19. є 6,06%. 12. LOC, NPM 17, 18 (.. 8 ),. M 0.0054,0. 2442, 19. є 9,61%. є є,. 2.12, 42,05% є 14,74%, 4,05%. 2.12 - є є 8-19, % 1-12,86 2 4,05 3-8,39 4 8,17 5 30,15 6 27,35 7 7,31 8-12,36 9 42,05 10 8,61 11 6,06 12 9,61. 42,05 є 14,74. 4,05

87 140,34 11,85. 2.12, (11,85) є.. 2.13. 2.13 -, % є (.1.6.2) 50 100 400 (.1.8.1) 19 49 87 TRW (.1.8.2) 14 31 44 ь, 1,40 8,19 16,31 ь, є - є 4,05 14,74 42,05 ь 2,73 11,47 29,18 11,27 19,53 14,82 2.13 Д8, 9]:,. є 11%,, TRW 49% 31%.

. 2.6. 88 Ві ві я 14 12 10 8 6 4 2 0 1 2 3 4 5 6 7 А і і і а і і 2.6-1 ь ь, 2 ь, 3 ь, 4 ь, 5 ь, 6 ь TRW, 7 ь ь І, 20%. З І. 2.5. : 1) ; 2), є ; 3)

; 4),. ( ). 89 2.5.1.. 2.7 є : 1. є є (2.3). 1.1 є. є :. 2.3.1, ;. 2.3.7,, є. 1.2 є є. є : є (2.3);,. 2.3.8. 2. є. 2.1 є (2.2) є. 2.2 є (1.2)., [57-63, 151, 152] є (,, ).

90 1 є 1.1. 2 1.2 є (4) 2.1 (4) 2.2 (5), (5) є 3 3.1 3.2 3.3 ь ь 4 4.1. 4.2. 4.3 ь ь ь 2.7-3. є. 3.1 є. 3.2 є (1.2).

3.3 є (1.3). є. 4. є. 4.1 є. 4.2 є. 4.3 є. є, (,,, )... 2.4.1,. 2.4.2 20%, ( TRW). є, є. є, є,. є., -, є є -, є. є. 91

2.5.2. є : 1., ; 2. є ґ ; 3. є ; 4. є,...,,,.. є.. 92 2.5.3.. 2.4.2 M 8-19.,

81%, 15%., 34%., є..,. 1.6.. 93 2, є, -,.. 1..,, є.. є ґ. 2.., 8,3%.

3. -... ; є ;. 4., є,,, є 20%. є,,.. 5.,.. 94

95 3 ( ).,,. 1.6.,,.. [57-61, 138, 151], є. є, є [62, 63, 67, 152-153]. є,. 2.4.1, 2.4.2 11%. '.,,..

96 3.1.,. 2.3,. 2.3.3.,... 3.1, є є.. 3.1 : є (, 3 4; 7 8); є (, 12 13); : WMC є 7 9; RFC є 4 133; LCOM є 14 166.,. 3.1 - WMC RFC LCOM 1 7 4 18 1 2 7 4 26 5 3 7 6 14 0 4 7 6 16 2 5 7 8 21 1 6 7 12 29 4 7 7 17 51 1

.. 3.1 97 8 7 20 59 13 9 8 11 16 0 10 8 13 24 1 11 8 21 59 2 12 8 133 166 2 13 9 5 19 2 14 9 7 16 1 15 9 9 17 0 є,, ь * ( ) P ( ), - c i (i=1 n)., 10 P ( c 0 ) 0. 3, * P ( c 1 ) 0. 5, * P ( c 2 ) 0.1 P ( c 3 ) 0. 1., є,,,.,.. 2.3.3 M RFC, WMC, LOC, CE, NPM, LCOM, CBO,.. 2.3.6,,... * *

є. є. є,.. є :,, (. 1.1), (. 1.4). є, [154, 155]. є, І, І. є :. є. ;,,.,,, i.,, P ( ) i. * 98 c i

3.2. є. 1.. 1.1. M RFC, WMC, LOC, CE, NPM, LCOM, CBO. 1.2. A pred 99 A base A, : A A A, A A 0. base A base.. 1.3.. A base pred base pred A base 1.4., є... A. 1.5., є N gr 1 3.322 lg N, N. A.. A pred. 2.. A base

2.1. i (Т=0,1,,n) 2.2. Ф=1,,m) 2.3. 100 A base nbi A base nb k (k - ь ь, A pred np k. : nb k nb i ; N base nb k ; N pred np k n i 0 3.. m k 1 3.1. i k A base m k 1 3.1 P * k nbi ( ci ) nb k 3.2 nb i i, nb k k. C c c,..., c i,... c n P ( ) 0, 1 * k c i i- (Т=0 n). P ( ) 1; 3.2. j A base M k i 0 n i 0 ( c ) ( c ) P ( c ) 3.3 j n i 3.3. (Part of Defectiveness Components) * k i NDbase PDC 100% 3.4 N 3.4. k base * c i A pred

101 nd * k M ( c ) np nb k k k j k A pred 3.5, A pred ND * m 3.5. k 1 * nd k 3.6 A pred ED (t) ED( t) ND * Size l soft nd * l ; ED ( t) DD 3.7 result ED(t) -, (1), DD result, l. A pred * nd l, KLOC, KLOC 400, ND * 512, ED ( t) 512/ 400 1. 3. DD 0. 5 ( 1.3 0.5) 400 280. Gr =д8,9,10ж, nd 323, 323>280. select 10 k 8 : 10 * k result DD result, * 512 nd k k 8 512 323 189 ED ( t) 0.47; ED ( t) DDresult ; 0.47 0. 50 400 400 400 3.6. A pred (Estimation of Number of not revealed defects) * EN( t) ND N ( t) 3.8 N rev (t) -, A pred ; rev

3.7. ER (t) (2).. 3.1. 102 1. 2. 3. 1.1 1.2. 1.3 A base 1.4 2.1 - A base 2.2. A base 2.3. A pred 3.1. - 3.2. - 3.3. 3.4., 3.5. 3.6., - 1.5 3.7. 3.1 -. A base A pred, -. i P ( ) (3.2) (3.4). * c i

j 103 A base (3.3). є (3.4), (3.5). є. є. 3.2. A base i (i= 0,1,,,,n) Abase A pred i A base (3.2) A base (3.4) 1- i A base (3.3) A pred (3.4), (3.5) 2- A pred (3.7) A pred (3.8) 3.2 - є. 3.3. є 1.1, є. 2.3.3 M RFC, WMC, LOC, CE, NPM, LCOM, CBO,. 1.4.2.

. 104 3.3.1. є є є A 1.2. A base A pred,. N base A base, N pred A pred. N base N 10%. N base DN base. DN base w, [145, 156] N base w(1 w) Nbase w (1 ) (3.9) N N base proj w t w (3.10) t -, (, P 0. 95 t 1. 96 ). є w. w є, є, є. N base, w, t P N base 2 t w(1 w) N proj 2 2 (3.11) N t w(1 w) w proj, є w, N base.

,. A base M RFC, WMC, LOC, CE, NPM, LCOM, CBO A pred. 1.3 є 105. A base є. 1.4 є. 3.3.2.,.. 2.3.3 M RFC, WMC, LOC, CE, NPM, LCOM, CBO. є, є. є,, є,. є,. 2.3.3, 2.3.5, 2.3.6 [8, 9, 16], є : 1.. є : 1.1. є є R M i,m 1 R N d, ; M i

1.2. 106 R N d, < 0.29. M i '.. 1.3. R M i,m 1,., R 0. 7,., M i M1 R N d,., є M i. 2.. (Complexity Indicator) CI k M... k n M, 1 1 n, n k,..., M 1,..., M - 1 kn- є.. [17] є R,, є N d CI R N d,. M i [90]. STATGRAPHICS PLUS [147] є,. 3.2., є R,, є N d CI R N d,. є M i

є,., [17],. 107 3.2 - R N CI d, R N d, M i 1 0,029 CBO + 0,009 LCOM + 0,0282 CE 0,81 0,73 0,08 2 0,049 CBO + 0,008 LCOM + 0,015 RFC 0,84 0,71 0,13 3-0.115 WMC + 0.293 NPM + 0.005 LOC 0,76 0,36 0,40 4 0,036 CE + 0,001 LOC 0,62 0,48 0,14 5-0,015 WMC + 0,027 RFC 0,64 0,49 0,25 6 0.024 WMC + 0.026 RFC + 0.0006 LCOM 0,77 0.57 0,20 7 0,009 RFC - 0.005 CE 0,55 0,46 0,09 8 0.014 RFC + 0.009 CE 0.66 0.47 0,19 9 0.011 RFC + 0.0005 LOC 0.71 0.46 0,25 10 0.016 CE + 0.0007 LOC 0.73 0.39 0,34 є є 0,21,, є є.. 3.3.3. N є Д145] N gr 1 3.322lg N (3.12) (Number Components) є

N 108 NCgr (3.13) N gr N gr NC gr.., A base A pred.. 3.4. : 1. M ( c j ) є M c ). є ; ( j 2. * ( P ( ) 1) c i, ; 3. ( P ( ) 0).,. * c i

109 4. * k * nd, ND,, ; 5. PDC є.,, [58, 59, 63], є,,,. є., PDC 60%, 60%.. 60%. є ; 6. EN (t),., 50. 3, 50 3=150 /. 7 /, 50 7=350 /. є. 7., ER (t) є (,, ). є,.,,,

..,, є,. є,,,,,,,. є,., є,. 110 3.5., (3.8)-(3.13),,.

3.5.1. є (. 3.2), (3.1)-(3.8), : 1. M c ) ( j. * k c i P ( ) (3.2),, 111, є, є. 2. ь * nd k, * ND, є, EN (t), ER (t). 3. PDC є є.,.. 3.5.2. є - є, - PROMISE [90]. є : 1.. 3.3.1,.

2.. 3.3.2. 3.. 3.3.2.. 4...2. 5.. ( ) [145],,, є, 2 ( ) є ( ) [156].,.,. 4.2.2. є є.. 112 3.5.3. Д90Ж ',. 3.3. 3.3 - -.. 1 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/бКХКЧ 2.6. 412 885 625 1,52 2 http://coно.ршшрхо.мшц/щ/щrшцтьонкэк/атфт/бкхкч 2.7 413 910 1213 2,94 3 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/бОrМОЬ 1.4 141 588 1596 11,32 4 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/ХЮМОЧО 2.2 64 247 414 6,47 5 http://code.google.com/p/promisedata/wiki/pro452 98 660 79 0,81 6 http://code.google.com/p/promisedata/wiki/pro285 1508 8718 1362 0,90 7 http://code.google.com/p/promisedata/wiki/pro265 1604 10274 1640 1,02 8 http://code.google.com/p/promisedata/wiki/ant, 1.7 209 745 338 1,62

113.. 3.3 9 http://мшно.ршшрхо.мшц/щ/щrшцтьонкэк/атфт/мкцох, 1.2 66 608 522 7,91 10 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/МКЦОХ, 1.6 113 965 500 4,42 11 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/МКЦОХ, 1.4 196 1745 670 3,42 12 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/бКХКЧ 2.5 305 803 531 1,74 13 http://code.google.com/p/promisedata/wiki/tomмкэ 6.0 122 858 114 0,93 14 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/ЩШТ 2.5 120 386 496 4,13 15 hээщ://мшно.ршшрхо.мшц/щ/щrшцтьонкэк/атфт/хюмочо 2.4 103 341 632 6,14 : 5474 28733 10732, ++, Java 28733. є., 10732.,. 2.3,..,,. 4.2.2. І 1. 885 412, 625 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/бКХКЧ 2.6. STATGRAPHICS PLUS [143]. 3.3.2 =(0.0106 RFC+0.0005 LOC) 100). A base 300. :, 8, (..1 ); (..2,.3 ) (..4 ) A pred, є 585 ; -, є,,,

,..5. -5,48%, 1,33%, 8,83%. -5,83%, 3,01 8,37%. 3,10%, -7,89 9,88%. І 2. 910 413, 1213 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/бКХКЧ 2.7. 114 A base 100. A pred, є 810,,..6. -3,76%, 3,22%, 8,77%. -2,85%, 3,09 8,94%. -0,40%, 0,40 0,84%. І 3. 588 141, 1596 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/бОrМОЬ 1.4. A pred A base 200., є 388,,..7. 2,55%, 1,51%, 19,39%. 2,54%, 1,17 18,86%. -2,25%, 2,61 8,70%. І 4. 247 64, 414 СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/ХЮМОЧО 2.2. A base 120.

A pred 115, є 127,,..8. 4,17%, -10,31%, 23,89%. 0,66%, -6,47 18,73%. 4,34%, -4,29 19,76%. І 5. 660 98, 79 http://code.google.com/p/promisedata/wiki/pro452. A base 200. A pred, є 460,,..9. є (79,. 3.3), (0,81,. 3.3)., є. 5.. є. -5,88%, -8,91%, 30,48%. 19,23%, -16,60 41,49%. -33,18%, 31,42%. І 6. 8718 1508, 1362 http://code.google.com/p/promisedata/wiki/pro285. A base 2000. A pred, є 6718,,..10.,, є. 5.

-7,69%, 7,18%, 13,84%. -7,33%, 4,91 15,55%. 10,38%, -12,78 26,51%. І 7. 10274 1604, 1640, http://code.google.com/p/promisedata/wiki/pro265. A base 2000. A pred 116, є 8274,,..11. 13,33%, - 5,15%, 10,70%. 3,10%, -5,41 14,38%. 24,86%, -26,18 26,18%. І 8. 745 209, 338, http://code.google.com/p/promisedata/wiki/ant, 1.7. A base 200. A pred, є 545,,..12. 5,88%, 5,29%, 12,28%. 4,42%, 5,98 9,98%. -3,60%, 0,37 21,30%. І 9. 608 66, 522, http://code.google.com/p/promisedata/wiki/camel, 1.2. A base 100. A pred, є 508,

,..13. 0,00%, 7,11%, 17,61%. -2,33%, 6,49 18,04%. 3,26%, 2,49 5,43%. І 10. 965 113, 500, http://code.google.com/p/promisedata/wiki/camel, 1.6. A base 200. A pred 117, є 765,,..14. 3,57%, 6,06%, 13,38%. 4,52%, 6,35 13,57%. -7,42%, 9,41 14,72%. І 11. 1745 196, 670, http://code.google.com/p/promisedata/wiki/camel, 1.4. A base 500. A pred, є 1245,,..15. -2,44%, 11,07%, 14,16%. -1,14%, 9,76 15,97%. -9,46%, 15,98 16,53%. І 12. 803 305, 531, СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/бКХКЧ 2.5. A base 200.

A pred 118, є 603,,..16. 3,03%, - 1,96%, 6,89%. 2,01%, -2,93 7,91%. 2,84%, -2,43 7,33%. І 13. 858 122, 114, СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/ЭШЦМКЭ 6.0. A base 200. A pred, є 658,,..17. є, (0,93. 3.3)., є. 3.. - 19,44%, 10,36%. - 18,89%, 13,32%. - 18,85%, 8,10 15,90%. І 14 386 120, 496, СЭЭЩ://МШНО.РШШРХО.МШЦ/Щ/ЩrШЦТЬОНКЭК/аТФТ/ЩШТ 2.5. A base 100. A pred, є 286,,..18. -12,69%, 9,38%, 16,37%. -12,83%, 8,96 17,29%. -7,92%, 5,50 12,76%.

І 15. 341 103, 632, http://code.google.com/p/promisedata/wiki/lucene 2.4. A base 140. A pred 119, є 201,,..19. 2,30%, - 4,68%, 8,63%. 6,67%, -2,65 9,41%. 7,74%, -7,64 12,40%.,,. 3.5.4.. 3.4.. 3.4 5. є 30,48%, 32,30%., (79,. 3.3), (0,81,. 3.3).. 0,81 є 30 %.. 3.5.

3.4 - %.% 2 є...% 2 120 є.. 1 0,69 0,73-5,48 1,33 119,63 10,94 8,83 20,11 4,48 2 1,28 1,33-3,76 3,22 101,12 10,06 8,77 34,56 5,88 3 2,81 2,74 2,55 1,51 613,57 24,77 19,39 240,06 15,49 4 1,75 1,68 4,17-10,31 790,08 28,11 23,89 325,45 18,04 5 0,16 0,17-5,88-8,91 1043,45 32,30 30,48 194,02 13,93 6 0,24 0,26-7,69 7,18 275,80 16,61 13,84 56,79 7,54 7 0,17 0,15 13,33-5,15 240,00 15,49 10,70 14,57 3,82 8 0,54 0,51 5,88 5,29 646,94 25,44 12,28 406,39 20,16 9 0,82 0,82 0,00 7,11 441,01 21,00 17,61 102,19 10,11 10 0,58 0,56 3,57 6,06 283,70 16,84 13,38 141,54 11,90 11 0,40 0,41-2,44 11,07 250,90 15,84 14,16 121,98 11,04 12 0,68 0,66 3,03-1,96 56,55 7,52 6,89 12,90 3,59 13 0,29 0,36-19,44 10,36 146,17 12,09 10,36 146,17 12,09 14 1,17 1,34-12,69 9,38 246,70 15,71 16,37 66,80 8,17 15 2,67 2,61 2,30-4,68 82,07 9,06 8,63 29,44 5,43 є 6,17 6,04 306,73 16,39 13,22 122,78 9,84 3.5 - % є є.% 2...% 2.. 1 388 412-5,83 3,01 101,16 10,06 8,37 21,60 4,65 2 1056 1087-2,85 3,09 103,98 10,20 8,94 33,68 5,80 3 1102 1075 2,54 1,17 709,85 26,64 18,86 247,18 15,72 4 214 213 0,66-6,47 623,51 24,97 18,73 314,56 17,74 5 62 52 19,23-16,60 1695,28 41,17 41,49 249,39 15,79 6 1011 1091-7,33 4,91 347,55 18,64 15,55 129,77 11,39 7 1332 1292 3,10-5,41 275,14 16,59 14,38 97,55 9,88 8 260 249 4,42 5,98 553,16 23,52 9,98 357,72 18,91 9 419 429-2,33 6,49 419,63 20,48 18,04 49,43 7,03 10 416 398 4,52 6,35 322,88 17,97 13,57 135,09 11,62 11 476 481-1,14 9,76 419,43 20,48 15,97 217,07 14,73 12 407 399 2,01-2,93 74,46 8,63 7,91 20,41 4,52 13 73 90-18,89 13,32 127,13 11,28 13,32 127,13 11,28 14 326 374-12,83 8,96 278,67 16,69 17,29 59,96 7,74 15 400 375 6,67-2,65 164,96 12,84 9,45 52,87 7,27 є 5,37 5,75 322,97 17,07 13,60 133,14 10,59. 3.5 5. (79

. 3.3), (0,81,. 3.3),. є. 0,81 є 40%.. 3.6.. 3.6 5.. є є. 0,81 є 30%. 121 3.6 -, %, % %.% є...% є.. 1 47,61 46,18 3,10-7,89 161,27 12,70 9,88 115,15 10,73 2 98,37 98,77-0,40 0,40 5,81 2,41 0,84 2,85 1,69 3 73,51 75,20-2,25 2,61 148,21 12,17 8,70 58,23 7,63 4 60,56 58,04 4,34-4,29 859,37 29,31 19,76 129,85 11,40 5 10,23 15,31-33,18 31,42 96,05 9,80 31,42 96,05 9,80 6 15,00 13,59 10,38-12,78 837,20 28,93 26,51 297,80 17,26 7 13,51 10,82 24,86-26,18 133,93 11,57 26,18 133,93 11,57 8 24,40 25,31-3,60 0,37 794,68 28,19 21,30 341,00 18,47 9 33,21 32,16 3,26-2,49 46,11 6,79 5,43 15,70 3,96 10 19,47 21,03-7,42 9,41 256,95 16,03 14,72 89,56 9,46 11 15,69 17,33-9,46 15,98 362,47 19,04 16,53 339,79 18,43 12 49,53 48,16 2,84-2,43 75,01 8,66 7,33 12,50 3,54 13 18,00 22,18-18,85 8,10 389,47 19,73 15,90 202,32 14,22 14 60,57 65,78-7,92 5,50 304,36 17,45 12,76 44,73 6,69 15 68,04 63,15 7,74-7,64 369,42 19,22 12,40 182,26 13,50 є 7,60 7,58 338,88 16,59 14,16 140,41 10,61 ь.. 3.3 ( ), ( ) ь.

122 Кі ь і ь ів 1,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0,2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 З АІС 3.3 -. 3.4 ь ь. 300 Кі ь і ь ів 250 200 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 З АІС 3.4 -. 3.5 ( %)., 5,. 3.7.

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/. 125 3.6. є. є : - 2%, 7%, 5%,., є. є є є., є,,, є., ь, І є. є, є 3.3.2 є - є.. 0,81 є, 30%. 3, є : 1., є

,., є. 2.,. - 2%, 7%, 5%,, є,.. 3.,,,,.. 0,81 є 30%. 4.,.. 126

127 4, є - -. є -. [158],,. є є, є, є є. є,,. є,,. є : 1. є,.

2. є, є ; 3. ; 4. є ; 5. є є ; 6. ; 7. є.. 128 4.1. [159], (. Conceptio - ) є,,,. є,. 4.1.1.,.. є [21], є, є. ь є, є. ь

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[97, 98], [99-103] [51, 104-109].. 1.7. І. IEEE 1061 [111], є.. 1.7.1. [107, 113-116 ] [7, 8, 9, 10, 16, 17].. 2.3. [90, 126 140, 114, 143, 145],,., є...1.1,.. 1.3. 1.5 Д48, 107, 113, 125, 126Ж Д7, 8Ж,... 2.3 Д8, 9, 11].,. 3.3.2 Д17Ж. 2 3, є.. є,. є. є.. 1.6. [47, 49, 56, 58, 130

60, 64] [126-140],., є :. 2.3.7 [9, 18] ;. 2.5 [18] ;. 3.3.2 [17] ; 3 [10, 19] ;,, є.. є VТЬЮКХ FШбPrШ 9.0. є : 1., є ; 2., є ; 3., є ; 4.,. 131

,.. 132 4.1.3.. 4.2.. 4.1.4.,. 4.2, є. є. є.,. 2.3.1... є,. 2.5. є -... 4.2..,. 4.2, є,. 2.5. є,. 2.3.7,, є. (2.6), є є (2.5), (2.5), (1.2).

133 ь І І 1. (. 1,2,3 ) - 2. (.4,8 ) - 1. 3. - 2. 4.? 5. (.6,8 ) 6. 7.? 8., (.8,9 ) - - -, 3. 4. 5. 6. ь ь, 4.2 -

-, [57-63, 151, 152], є (,, ).. 4.2.,,.. 4.2.. - є -. є.. 4.2.. є. ь є... 3.3.1.. 3.3.1 - є,. - є. є, 3. є,.. є,,., 134

,.. 4.2..,. є. є.. 4.2..,, є. ь є є. є,. є,,.. 4.2.. ь є,.,. є.. 4.2..,. 4.3, є IDEF (Integrated computer-aided manufacturing DEFinition). [160] IDEF є,. є є, 135

, ',, '.,,,. IDEF є IDEF0, IDEF1.. IDEF0 є, є, 'є,.. 4.3. IDEF0. 136 ISO/IEC 9126-1:2001 ISO/IEC 9126-3:2003 ISO/IEC 9126-2:2003 ISO/IEC 25010:2011 ISO/IEC 29119 0 1 2 3-0 4.3-0 '....,. є, є.

є є... 4.4-0. 137 C1 I1 I2 1 2 O1 M1 2 ISO/IEC 29119 M2 3 ISO/IEC 29119 O2 M3 4 M4 O3 0 4.4-0 4.4. : 1 ; 2 ; 1, ISO/IEC 9126-1:2001, ISO/IEC 9126-3:2003, ISO/IEC 9126-2:2003, ISO/IEC 25010:2011; 2 ; ; ; ; 1, 2, 3, ; 4, ; 1 ; 2 ; 3.. 4.4 є,

,,.. 2.5,. 3.1.,.. 1.1. 1.1,. 138 4.1.5. є є є.,,.. 4.5. І 4.5 -

, є., є є. є.,,..,..,. є,.,.. 4.2, :,,,. є.,... - є 11%. - є 139

,.,, є,. є 9%. є., є 1000., 100 300. є є - є,., є - є,. є, є. (waterfall model) [149]. є,. є. є. (iteration model) [150]. є. є.,,,. є 140

, ь,,.,. 141 4.1.6. є : 1. ( 11%, 20% ) є є ; 2. ( 9%), ; 3. ; 4. ; 5.. 4.2.. є VТЬЮКХ FШбPrШ 9.0.. 4.6. є, є,, є.

142 4.6 ( ) (, ), є :.,.1.4.2,, [117-121], (HTML, CSV, XML).

. EМХТЩЬО MОЭrТМЬ PХЮРТЧ Д120Ж.. 4.7 є, є.. 4.8 є.. 4.9. 143 4.7 Eclipse Metrics Plugin., Rational ClearQuest [161], Borland StarTeam [162], Ticket Tracking [163], Mozilla Bugzilla [164], Trac, TUTOS. Bugzilla Mozilla ( Perl,, Mozilla Public License). Web- є. 4.10. є

(HTML, CSV, XML).. 144 4.8 Eclipse Metrics Plugin 4.9 Eclipse Metrics Plugin

145 4.10 Web - Bugzilla Mozilla ( ),. PVCS Version Manager [122], Microsoft Visual Source Safe [123], SoftBench CM, SoftStatic, Development Manager Hewlett-Packard [124], RCS, CVS, Subversion (SVN), Mercurial, Git, Bazaar. Subversion (, FreeBSD, Linux, Mac OS X, Windows, Apache License).

.. 146 4.2.1.. 4.11. ь ь, І 4.11 -,. є є..

4.2.2.. 4.6... є є, ( 1,. 4.6.) ( 2). 3 є,..,.. dbf, Visual FoxPro 9.0. є ( 4). є є,.,,, є. є є ( 6).,, ( 8), ( 9). є... 147

, є ( 5) ( 7),. є.. 148 4.2.3.. 4.12. (. 4.12 1) є : 1. ; 2.. 3.. (. 4.12 2) є : 4. ; 5. ; 6. ; 7. ; 8.. (. 4.12 3) є : 9. : 10. ; 11. ;

12.,,..1.5. є. 149.1. 1 2 1.2. 3 4.3. 5 : - - 2.4...5...6...7...8. 6 7 8 9 10 11 12 13 14 : - - - -.9. 15 3.10. 16.11. 17.12. 18 4.12 - є. 1 є. 2 є. 3 є є. 4 є. 5 є. 6 є. 7 є є. 8 є (Complexity Index)

CI k M... k n M 1 1 n, n, 150 M 1,..., M - k,..., 1 kn - є. 9 є.3.3.1. 10 є.3.3.3. 11,12,13. 11 є.. 1. 12 є.. 2. 13 є.. 3. 14 є. 15-17. 15 є. 16 є.. 4. 17 є.. 5. 10-13 є. є 7.. 18.. 4.2.4., є : 1 є...1..

2 є...2.. 3 є...3.. 4 є,...4.. 5 є :,,.,,, є, 2...5..,,., є.,,,. -. - є -.. 151 4.3. є «є» «

є»,., є,,,, -. «є»,,,.,,.,, ; ; ;,,,,. 6.. 152 4.4. є. є, є. ( ), [165, 166]

, є., ( ) 4-400 / 40-500., ( ).,.,,. -,,,,,,,.. (,,,,, ); (,,, ). є : 1. «З» є. є,,.,,, ; 2.. є, є є. є є. є,., ; 3.. є. 153

є ( / ) ; 4.. є. є.,,,. Case- : ; ; ;..,. 4.2,.,. 4.2, :, 2.3.7,, ; ; (2.3). 2.3.2, є (2.5) ;, 154

. (2.2) є. ь,,..,. : (. 3.3.2), є ;. 3.3.3.,, ;..,.3.4,. ь,. 4.2, -,.,,,. ь..,.. 12,8%. 155

11,2%. ( )... 8%.,, 6,5%,., є.,. 7. 156 4.5.,. є 7%,, 8,8%.. 10,3%.,.

( 6,3%) ( 8,6%). є,. є.,. 8., є - -, є,,, 7%,. 8%. 157 4, є. є - -. : 1., є,. є. 2., є

. 3., є,. 4., «є». 5. є -, 7%,. 8%. 158

-, є,,. є.,, є є. : 1.,,.,,. є, є,.,. 2. є.,,. 159

3.. є,. 4.., є,,. 2,73%, є 11,47%, 29,18%. 20% ( TRА). 5., є,. 2%, є 9%, 20%. 6... 7., «є»,,. 160

8. є є.,, 7%,. 8%. - є,. 12,8% 10,3%. 11,2% 6,3%.. 161

1.,.. Д Ж /..,..,.. //. 6(47). :, 2010.. 204 210. 2.,.. Д Ж /..,.. //. 76..:, 2010.. 68 79. 3.,.. Д Ж /..,..,.. //. 01 (77)..:, 2010.. 93 101. 4.,.. Д Ж /.. //. 02 (78)..:, 2011.. 109 116. 5.,.. Д Ж /..,.. // :... -. 2011.. 1(35).. 82 85. 6. є.. /.. є,.. //.. -. 2011,. 1,. 169 172. 7.,.. Д Ж /.. //. 5(57). :, 2012.. 212 218. 8.,.. Д Ж /..,.. // 162

. 07 (83)..:, 2012.. 113 120. 9.,... Д Ж /..,.. //. 4(56). :, 2012.. 73 80. 10.,.. Д Ж /..,.. // :... -. 2013.. 1(40).. 79 84. 11.,.. Д Ж /.. //. 5(64). :, 2013.. 414 420. 12.,.. Д Ж /.. //. 5(69). :, 2014.. 60 64. 13.,... /..,.. // - TAAPSD 2010. : 2010.. 332 340. 14.,.. Д Ж /... // :... :, 2011.. 111. 15.,.. Д Ж /.. // - «163

» TAAPSD 2011. : 2011.. 210 217. 16.,.. /..,... // :... :, 2012.. 25. 17.,.. Д Ж /.. // :... :, 2014.. 382 389. 18. Maevsky, D. A. A method of a priori software reliability evaluation [Electronic resource] / D. A. Maevsky, S. A. Yaremchuk, L. N. Shapa // Reliability: Theory & Applications. 2014. Vol. 9. 1(31). p. 64 72. Access mode: http://www.gnedenko-forum.org/journal/2014_1.html 19. Yaremchuk, S. A. The Software Reliability Increase Method / S. A. Yaremchuk, D. A. Maevsky // Studies in Sociology of Science. 2014. 5(2). p. 89 95. Access mode: http://www.cscanada.net/index.php/sss/article/view/4845. DOI: http://dx.doi.org/10.3968/4845 20.,.. Д Ж /.. //, :... :, 2014..169 173. 21. є.. :... /.. є ;... -., 2013. 440. 22..... /..... :, 2001. 259. 164

23... /.... :, 2004. 288. 24... /.... : -, 2002. 260. 25... : /..,.... :, 1996. 240. 26... : /.... :, 2003. 365. 27. Д Ж : http://www.cusoft.ru/error.php. 28. Anthes G. H. IRS Project Failures Cost Taxpayers $50B Annually / G. H. Anthes // Computerworld. 1996. VШХ. 30, 42. P. 28-29. 29.. /... : -, 2007. 240. 30. Neumann P. G. Computer-Related Risks / P. G. Neumann. Addison-Wesley, 1995. 384 p. 31... :.. /..,.. є -.. :, 2004. 187. 32... /.... :, 2004. 688. 33... /.... : -, 2008. 106. 34.. /.,. //.. 2004. 1.. 18 21. 35. [ Ж : СЭЭЩ://СОХЩinform.ru/ lassif_inf_system_po_priznaku_struk_zadach.htm. 165

36.. /. // PC АООФ RОЯТОа. 1998. 44.. 44 49. 37... //. 2003. 20.. 8 11. 38.,.. /... //. 2012. 7. : Д... Ж,. 99 103. 39. є є. Д Ж : http://ubr. ua/uk/finances/macroeconomics-ukraine/v-ukran-zarestrovano-blshe-mliona-pdprimstv- 82429. 40... /.. //. 1998. 10.. 4 7. 41. ISO/IEC 9126-1:2001. 1:. 42. ISO/IEC 9126-2:2003. 2:. 43. ISO/IEC 9126-3:2003. 3:. 44. ISO/IEC 25010:2011.Systems and software Quality Requirements and Evaluation (SQuaRE) System and software quality models. [Electronic resource]. Access mode: http://www. standards. ru/document/4580604.aspx. 45. 28806:90... Д Ж : http://vsegost.com/catalog/10/10605.shtml. 46. 28195:89.... : -, 1989. 47.... /.... :, 2001. 307. 166

48.. /.,.,... :, 1981. 326. 49... /..,..,..,...,, 2008. 99 c. 50...... /..,..,... :.. -, 2004. 159. 51... /. //. 2013. 1.. 117-121. 52. IEC 61513:2001 Nuclear power plants - Instrumentation and control systems important for safety - General requirements for systems ( - -, ). 53. IEC 60880:2010 Software for computers in the safety systems of nuclear power stations ( ). 54. IEEE Std. 982:2005 IEEE Standard Dictionary of Measures of the Software Aspects of Dependability. 55. IEEE Std 610-12:1990 IEEE Standard Glossary of Software Engineering Terminology. 56... :.... /..,..,.... :, 2003. 284. 57.. /... : &, 2006. 240. 58.. r /... :, 2007. 297. 59... - /... :, 2005. 896. 167

60... - /.,.,... :, 2001. 544. 61...,,, /... :, 2006. 566. 62.. : - /... :, 1979. 321. 63... /... :, 2007. 312. 64... /..,.... :, 1992. 78. 65. Haag S. Quality Function Deployment. Usage in Software Development / S. Haag, H. K. Raja, L. L. Sekade // CШЦЦ. П ACM 1998. 1.. 39. 66. Goel A. L. Software reliability models: Assumptions, Limitations and Applicability / A. L. Goel, K. Okumoto // IEEE Transactions on Software Engineering. 1985. 11.. 1411 1423. 67... : Д Ж / [..,..,...] ;....,... [..], 2007. 244. 68. Lyu M. R. Handbook of Software Reliability Engineering / M. R. Lyu. McGraw-Hill Company, 1996. 805 p. 69. Moranda P. B. Final Report of Software Reliability Study / P. B Moranda, J. Jelinski // McDonnel Douglas Astronautic Company. 1972. RОЩШrЭ 63921. 70. Wolverton R. W. Assessment of Software Reliability / R. W. Wolverton, C. J. Shick // Annual Reliability and Maintainability Symp. 1977. P. 485 488. 71. Sukert C. A. An investigation of software reliability models / C. A. Sukert // Proc. Annual Reliability and Maintainability Symp. 1977. P. 478 484. 72. Musa J. D. Validity of Execution time theory of software reliability/ J. D. Musa // IEEE Trans. on reliability. 1979. 3. P. 199 205. 168

73. Levendel Y. Reliability analysis of large software systems: defect data modeling / Y. Levendel // IEEE Transactions on Software Engineering. 1990. 16. P. 141 152. 74. Duan J. T. Lourning Curve Approach to Reliability Monitoring / J. T. Duan // IEEE Trans. on Aerospace. 1964. 2. P. 563 566. 75 Musa J. D. Logarithmic Poisson Time Model for Software Reliability Measurement / J.D. Musa, K. A. Okumoto // Proc. Seventh International Conference on Software Engineering. 1984. P. 230 238. 76. Moranda P. B. Probability-Based Models for the Failures During Burn / P. B. Moranda // Joint National Meeting ORSA. 1975. 77. Yamada S. S-shaped software reliability grows modeling for software error detection / S. Yamada, M. Ohba, S. Osaki // IEEE Trans. Reliability. 1983. 5. P. 475 478. 78. Musa J. D. Software Reliability Models: Concepts, Classification, Comparisons, and Practice / J. D. Musa, K. Okumoto // Electronic Systems Effectiveness and Life Cycle Costing, NATO ASI Series, F3. 1983. P. 395 424. 79. Amrit L. Software reliability models: Assumptions, limitations, and applicability / Amrit L., Goel //I EEE Transactions on Software Engineering. 1985. 12. P. 1411-1423. 80. Albrecht A. J. Measuring Application Development Productivity / A. J. Albrecht // Proceedings of the JШТЧЭ S R /GUID /IB AЩЩХТМКЭТШЧ Development Symposium. 1979. P. 83 92. 81. International Function Point Users Group Д Ж : http://www.ifpug.org. 82. Jones C. Applied Software Measurement: Global Analysis of Productivity and Quality Category. Quality Control Publisher / C. Jones. McGraw Hill Osborne Media, 2008. 662. 83. Gaffney J. Estimating Software SТгО ПrШЦ CШЮЧЭЬ ШП EбЭОrЧКХЬ, GОЧОrКХТгКЭТШЧ of Function Points, / J. Gaffney, R. Werling // Software Productivity Consortium document number SPC-91094. 1991. 169

84. ISO/IEC 20926:2003. Software Engineering IFPUG 4.1 Unadjusted functional size measurement method. International Organization for Standardization, 2003. 85. Vogelezang F.W. One year experience with COSMIC-FFP / F.W. Vogelezang, A.J.E. Dekkers // Software Measurement European Forum. 2004. Rome (Italy). 86. Akiyama F. An example of software system debugging / F. Akiyama // Information Processing. 1971. 71. P. 353 379. 87. Gaffney J. R. Estimating the Number of Faults in Code / J. R. Gaffney // IEEE Trans. Software Eng. 1984. 10. P. 357 362. 88. Compton T. Prediction and control of Ada software defects / T. Compton, C. Withrow // Systems and Software. 1990. 12. P. 199 207. 89. Lipow M. Number of Faults per Line of CODE / M. Lipow / IEEE Trans. Software Engineering. 1982. Vol. 8, 4. P. 437 439. 90. The PROMISE Repository of empirical software engineering data Д Ж : https://code.google.com/p/promisedata/w/list. 91. GХТЛ. CШЦЩОЭТЭТЯО EЧРТЧООrТЧР: HКЧНЛШШФ ПШr SвЬЭОЦЬ EЧРТЧООrТЧР, Requirements Engineering, and Software Engineering using Planguage /. Glib. Amsterdam. : Netherlands: Elsevir, 2006. 496 p. 92. ISO/IEC 2382-1:1993. Information technology Vocabulary Part 1: Fundamental terms. 93... :, 2003. 1437. 94.... /.... : -, 2000. 72. 95.. - ++ /... :, 1998. 560. 96. Dijkstra E. On the Cruelty of Really Teaching Computer Science / E. Dijkstra // Communications of the ACM 32. 1989. 12. P. 1398 1404. 170

97. Miller G. The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information / G. Miller // The Psychological Reviev. 1956. 63. P. 81 97. 98. Simon H. The Sciences of the Artificial / H. Simon. Cambridge : The MIT Press, 1982. 81 p. 99... :. /... :.., 2005. 291. 100.... /.. //. ( ). 1970. 2.. 98-105. 101... /.... :, 1975. 193.. 102.... /..,.... :, 1963. 252. 103... /..,... :, 2007. 612. 104. Ε.Α. / Ε.Α., Η..,... :, 1989. 304. 105... - /.... : - -, 2007. 276. 106... /.. //. 1965. 1, 1.. 3 11. 107. McCabe T.A. Complexity Measure / T.A. McCabe // IEEE Transaction on Software Engineering. 1976. v. SE 2, 4. P. 308 320. 108. A. / A,.,... :, 1979. 536 c. 171

109... /.. //. 1999. 3.. 127 141. 110... /.... :, 1978. 399. 111. IEEE Std. 1061:1998. IEEE Computer Society: Standard for Software Quality Metrics Methodology, 1998 20 p. 112. TIOBE Software: Tiobe Index Д Ж : http://www.tiobe.com/index.php/content/paperinfo/tpci/index.html. 113... /.... :, 1981. 128. 114. Fenton N.E. Software Metrics: A Rigorous and Practical Approach / N.E. Fenton, S.L Pfleeger // International Thomson Computer Press. 1996. 2nd ed. PWS Publishing. P. 36-39. 115.. SЮЛЯОrЬТШЧ CХОКrCКЬО /.,.,. // [ Ж : http://www.cmcons.com/articles/cc_cq/dev_metrics/mertics_part_1. 116. Chidamber S. A Metrics Suite for Object-Oriented Design / S. Chidamber, C. Kemerer // IEEE Transactions on Software Engineering, 1994. 20, P. 476-493. 117. Ndepend Д Ж : http://www.ndepend.com/default.aspx. 118.. C# 5.0.NET 4.5 /... :, 2013. 1312. 119. Resource Standard Metrics Д Ж : http://msquaredtechnologies.com/m2rsm/docs/index.htm. 120. Eclipse Metrics Plugin Д Ж : http://eclipse-metrics.sourceforge.net. 121. IBM Rational Telelogic Logiscope Д Ж :: http://www.interface.ru/home.asp?artid=18833. 122. PVCS Version Manager Д Ж : http://pvcs.synergex.com/products/pvcs_version_manager.aspx. 172

123. Visual SourceSafe 6.0 Solution Center [ ] : http://support.microsoft.com/ph/3036. 124. HP SoftBench Software Д Ж : http://h20566.www2.hp.com/portal/site/hpsc/template.page/public/kb/docdisplay. 125. Kitchenham B.A. An evaluation of some design metrics / B.A. Kitchenham, L.M. Pickard, S.J. Linkman // Software Engineering. 1990. Vol. 5, No.1. P. 50 58. 126. Gao K. Comprehensive Empirical Study of Count Models for Software Fault Prediction / K. Gao, T. Khoshgoftaar // IEEE Transactions on Reliability. 2007. Vol. 56. P. 223 236. 127. Khoshgoftaar T. Software Quality Classifcation Modeling Using The SPRINT Decision Tree Algorithm / T. M Khoshgoftaar, N. Seliya // Proceedings of the 14th IEEE International Conference on Tools with Artifcial Intelligence, Washington, USA. 2002. P. 365 374. 128. Koru A. An investigation of the effect of module size on defect prediction using static measures / A. Koru, H. Liu // Proceedings of the 2005 workshop on Predictor models in sofэакrо ОЧРТЧООrТЧР. PRЇMISE 05, New York, USA. 2005. P. 1 5. 129. Ma Y. Statistical framework for the prediction of fault proneness / Y. Ma, L. Guo, B. Cukic // Advances in machine learning application in software engineering Idea Group Inc. 2007. P. 237 265. 130. Guo L. Robust Prediction of Fault-Proneness by Random Forests / L. Guo, Y. Ma, B. Cukic // Proceedings of the 15th International Symposium on Software Reliability Engineering, Washington, USA, IEEE Computer Society. 2004. P. 417 428. 131. Drummond C. Class Imbalance, and Cost Sensitivity: Why Under-sampling beats Over-sampling / C. Drummond, R. Holte // In Workshop on Learning from Imbalanced Datasets, Washington, USA. 2003. P. 1 8. 132. Drummond C. Severe class imbalance: Why better algorithms are not the answer / C. Drummond, R. Holte // Proceedings of the 16th European Conference of Machine Learning. Portugal, Springer. 2005. P. 539 546. 173

133. Yuan X. An Application of Fuzzy Clustering to Software Quality Prediction / X. Yuan, T. Khoshgoftaar, Allen E. // Proceedings of the 3th IEEE Symposium on Application-Specifc Systems and Software Engineering Technology, Washington, USA, IEEE Computer Society. 2000. P. 85 90. 134. Mahaweerawat A. Software fault prediction using fuzzy clustering and radial basis function network / A. Mahaweerawat, P. Sophasathit, C. Lursinsap // In International conference on intelligent technologies, Vietnam. 2002. P. 304 313. 135. Sahraoui H. Extending Software Quality Predictive Models Using Domain Knowledge / H. Sahraoui, M. Boukadoum // Proceedings of the 5th International ECOOP Workshop on Quantitative Approaches in Object-Oriented Software Engineering, Budapest. 2001. P. 139 152. 136. Wang Q. Extract Rules from Software Quality Prediction Model Based on Neural Network / Q. Wang, B. Yu, J. Zhu // Proceedings of the 16th IEEE International Conference on Tools with Artifcial Intelligence, Washington, USA, IEEE Computer Society. 2004. P. 191 195. 137. Thwin M. Application of neural networks for software quality prediction using Object-oriented metrics / Thwin M. M. T., Quah T.-S. // Journal of systems and software. 2005. Vol. 76, No. 2. P. 116-125. 138.,.. MatLab Д Ж /..,.. //. :. 2012. 5.. 219 224. 139. Munson J. C. The use of software complexity metrics in software reliability modeling / J. C. Munson, T. M Khoshgoftaar // Software Reliability Engineering Proceedings International Symposium. 1991. P. 2 11. 140. Fenton N. E. A Critique of Software Defect Prediction Models / N. Fenton, M. Neil // IEEE Trans. Softw. Eng. 1999. Vol. 25, 5. P. 675 689. 141..... /.,.... :, 2011. 576. 174

142. Д Ж : СЭЭЩ://НТМ.КМКНОЦТМ.rЮ/НТМ.ЧЬП/ОЧМИЦКЭСОЦКЭТМЬ/3687/. 143.... /.... : -, 2005. 288. 144... :.. /.,.... :, 1999. 296. 145.... /.... :, 1990. 191. 146.., /.,.,... :, 1989. 215. 147. Statgraphics Statistical Analysis and Data Visualization Software. Д Ж : http://www.statgraphics.com. 148.. /... :, 2003. 320. 149. Winston R. Managing the Development of Large Software Systems / R. Winston // Proceedings of IEEE WESCON. 1970. P.1 9. 150.. Scrum: Succeeding with Agile: Software Development Using Scrum /... :, 2011. 576. 151... /... :, 2004. 318. 152.. : /... :, 1985. 512. 153.,.. Д Ж /..,.. //. 2010. 6. :,. 211 218. 154... /.... :, 2005. 576. 175

155... :. /.... :, 2006. 479. 156... : /... :, 2003. 217. 157... :... /.... -., 2012. 167. 158. Д Ж : http://dic.academic.ru/dic.nsf/enc_tech. 159.. Д Ж : http://dic.academic.ru/dic.nsf/enc_philosophy. 160.,. 50.1.028-2001.. Д Ж : http://www.infosait.ru/norma_doc/48/48889/index.htm. 161. Rational ClearQuest and Rational ClearQuest ALM Download appliance [ ] : http://www.ibm.com/developerworks/downloads/r/rcq/index.html. 162. Why choose StarTeam Д Ж : http://www.borland.com/products/change-management/starteam. 163. Ticket Tracking System Д Ж : http://ticket-tracking-system.soft112.com. 164. Bugzilla Main Page Д Ж : https://bugzilla.mozilla.org. 165.... - /.... :, 2007. 240. 166.....,,, /.... :, 2010. 304. 176

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TIOBE Software Google, Google Blogs, Yahoo!, Wikipedia, MSN, YouTube, Bing, Amazon, Baidu. 184.12-1 C 17.809 % 2 Java 16.656 % є - є 3 Objective-C 10.356 % є - є 4 C++ 8.819 % є - є 5 PHP 5.987 % є - є 6 C# 5.783 % є - є 7 (Visual) 4.348 % Basic є - є 8 Python 4.183 % є - є 9 Perl 2.273 % 10 JavaScript 1.654 % є - є є - є

185.1 - є є є wmc dit noc cbo rfc lcom ca ce npm lcom3 loc 1 2 3 4 5 6 7 8 9 10 11 1 0,3842-0,0654 0,0150 0,2175 0,5969 0,3492 0,0222 0,3880 0,2627-0,0402 0,5177 2 0,2246-0,1824-0,0083 0,1242 0,2977 0,2455 0,0278 0,1958 0,1438-0,0723 0,3008 3 0,4966 0,1053-0,0040 0,1899 0,5727 0,3862-0,0235 0,4188 0,4333-0,1987 0,3019 4 0,5261-0,1325 0,0296 0,3103 0,6860 0,3787 0,1216 0,4377 0,4553-0,1378 0,3671 5 0,2125 0,3239 0,0000 0,0788 0,2891 0,1813 0,0140 0,1516 0,1814-0,3868 0,1304 6 0,2997-0,0160 0,0606 0,2957 0,4508 0,1406 0,1156 0,0000 0,2033-0,0755 0,4307 7 0,2320-0,0022-0,0290 0,1333 0,3720 0,1160 0,0789 0,2788 0,1141-0,0818 0,3539 8 0,0441 0,1377-0,0201-0,0171 0,1293-0,0169-0,1140 0,3142 0,0336 0,0441 0,0589 9 0,3256 0,1312-0,0191 0,0669 0,4958 0,2012-0,0501 0,4124 0,2788-0,0958 0,3304 10 0,6045 0,0021 0,0043 0,2775 0,6718 0,4918 0,1757 0,4615 0,5607-0,0805 0,5482 11 0,5620 0,0333-0,0153 0,2710 0,6535 0,5003 0,1796 0,5203 0,4871-0,0872 0,6485 12 0,1287-0,0472 0,1667 0,2510 0,1115 0,0494 0,2340 0,0638 0,1243 0,0451 0,0963 13 0,4122-0,0448 0,1762 0,1796 0,4439 0,4749 0,0891 0,3789 0,4060-0,0279 0,4341 14 0,3051-0,0141 0,1456 0,4631 0,2796 0,2409 0,4235 0,1990 0,2731-0,0524 0,2840 15 0,4482 0,0506 0,1656 0,2583 0,4287 0,4355 0,1921 0,3010 0,4358-0,0616 0,4156 16-0,2481 0,4851 0,0000 0,2461-0,0270-0,0914-0,6547 0,3658-0,4975 0,3496 0,3811 17 0,2972-0,0895-0,0624 0,2952 0,2929 0,2576-0,1579 0,3030 0,0988-0,0559 0,3372 18 0,0046 0,0303-0,0438-0,2572 0,0931 0,0413 0,0926-0,2818 0,1126 0,2257-0,0468 19 0,8285 0,0174 0,0637 0,6994 0,8650 0,8965 0,3526 0,7381 0,7935-0,0917 0,8851 20 0,5045-0,0136 0,2364 0,4226 0,5393-0,0034 0,2539 0,4825 0,5078 0,0046 0,5785 21 0,4112-0,0202-0,0160 0,3034 0,5095 0,4940 0,0684 0,4571 0,3055-0,0732 0,5221 22 0,4647 0,1317-0,0412 0,5315 0,6913 0,3733 0,4521 0,6074 0,7236-0,0923 0,6735 23 0,4996 0,0244-0,0432 0,6626 0,6870 0,2761 0,6177 0,6337 0,7204-0,1018 0,6784 24 0,4651 0,0382-0,0538 0,6477 0,6984 0,5052 0,5937 0,6392 0,6480-0,0753 0,6644 25 0,4980 0,0237-0,0299 0,6494 0,6534 0,3266 0,6151 0,5795 0,6546-0,0599 0,6004 26 0,1177 0,0605-0,0219 0,1746 0,1899 0,2254 0,1563 0,1937 0,1560 0,0271 0,1178 27 0,7200-0,0624 0,1370 0,6296 0,5825 0,7346 0,5453 0,4776 0,7152-0,0443 0,5288 28 0,7610-0,0492 0,2300 0,6795 0,6906 0,7100 0,5288 0,5999 0,7041-0,1072 0,5628 29 0,2479 0,0784 0,0851 0,2932 0,2108 0,0459 0,2134 0,2110 0,3693-0,1163 0,2759 30 0,1810-0,0736-0,0604 0,2267 0,4290 0,0539-0,0521 0,4389 0,1151-0,0703 0,4008 31 0,2476-0,1292-0,0455 0,2442 0,4547 0,2096-0,0537 0,4875 0,1629-0,1449 0,4549 32 0,2683-0,0461 0,0711 0,3199 0,4871 0,1539 0,0808 0,4368 0,1922-0,1716 0,4440 33-0,0216-0,2545 0,0430 0,1690 0,0384-0,0331 0,0814 0,1254 0,0739-0,1719-0,0382 34 0,4073-0,0703 0,1217 0,3641 0,5204 0,3253 0,1517 0,4650 0,3106-0,3314 0,4335 35 0,4786-0,1000 0,0091 0,2553 0,5253 0,4317 0,0647 0,4702 0,2684-0,1346 0,4883

186...1 36 0,5327-0,0885 0,1973 0,2095 0,5251 0,4957 0,0356 0,3503 0,5543 0,0251 0,3125 37 0,1072-0,1379 0,0072 0,0479 0,1598 0,1498 0,0083 0,1259 0,0306-0,0136 0,1545 38 0,4301-0,0253-0,0003 0,3822 0,5914 0,5214 0,0946 0,6510 0,1849-0,1893 0,2481 39 0,4478 0,0506 0,0634 0,5043 0,6945 0,3383 0,2143 0,6793 0,2801-0,2379 0,5484 40 0,3788-0,0019 0,0549 0,2235 0,4593 0,3076 0,1637 0,2215 0,3442-0,0854 0,4111 41 0,3433 0,0480 0,0690 0,2197 0,4689 0,3099 0,1215 0,2768 0,3039-0,0704 0,2888 42 0,3972 0,0259 0,0531 0,1925 0,4558 0,3170 0,1230 0,2033 0,3419-0,1693 0,4526 43 0,2433 0,0506 0,0230 0,2035 0,3292 0,1934 0,1226 0,2273 0,2088-0,0365 0,3888 44 0,6298-0,0708-0,0468 0,3524 0,6886 0,5513 0,1300 0,5560 0,5082-0,1240 0,3872 45 0,6746-0,0982-0,0263 0,2584 0,6522 0,6116 0,0843 0,4482 0,5601-0,0919 0,4883 46 0,6998 0,0597 0,0989 0,4807 0,7337 0,6545 0,2757 0,5547 0,6373-0,1303 0,5669 є 0,3744 0,0016 0,0378 0,2985 0,4646 0,3165 0,1480 0,3749 0,3366-0,0798 0,4002 є 0,8285 0,4851 0,2364 0,6994 0,8650 0,8965 0,6177 0,7381 0,7935 0,3496 0,8851 є 4 17 16 7 1 6 10 3 5 19 2.2 - є є є dam moa mfa cam ic cbm amc max_cc avg_cc 12 13 14 15 16 17 18 19 20 21 1 0,1089 0,0943-0,0985-0,2639-0,0297-0,0927 0,2035 0,2761 0,2395 342 2 0,0643 0,1500-0,2125-0,1294-0,1719-0,2072 0,1906 0,2003 0,1755 39 3 0,1245 0,2364 0,0816-0,2868 0,2350 0,2921 0,0390 0,3688 0,1615 496 4 0,1730 0,2443-0,1301-0,2642 0,0411 0,0554 0,0880 0,3491 0,1964 500 5 0,3339 0,2312 0,2211-0,2805-0,1652-0,1652 0,0541 0,1173 0,0825 11 6 0,1750 0,3149-0,0307-0,2206 0,1127 0,1796 0,1332 0,3007 0,2316 114 7 0,1459 0,1051-0,0309-0,3191 0,0370 0,0194 0,1867 0,1558 0,0645 33 8-0,0015 0,1289 0,1616-0,0237 0,0996 0,0388 0,0681 0,0641 0,0400 47 9 0,1751 0,3103 0,0676-0,2446 0,2535 0,1670 0,0044 0,1229 0,0260 35 10 0,1669 0,3811-0,0729-0,3636 0,1537 0,1836 0,0149 0,2614 0,1596 184 11 0,1632 0,3389-0,0408-0,3285 0,1274 0,1191 0,0544 0,4261 0,2548 338 12-0,0153 0,0772-0,0832-0,0929-0,0678-0,0613-0,0045 0,0319 0,0608 14 13 0,0483 0,2236-0,0212-0,2114 0,0522 0,1055 0,1201 0,1976 0,1891 522

...2 187 14 0,0548 0,2353-0,0585-0,1919 0,0011-0,0374 0,0580 0,2088 0,1618 500 15 0,1016 0,2047 0,0064-0,2341 0,1028 0,0841 0,0596 0,2278 0,1182 670 16-0,2683 0,4472 0,3344 0,6757 0,0000-0,3381 0,5288-0,5571-0,6083 3 17 0,1782 0,2865-0,1170-0,2238-0,0090-0,0583 0,0147-0,0955-0,0887 15 18-0,1611-0,1381 0,0453-0,1122 0,2672 0,5979-0,0697 0,2250 0,2871 6 19 0,1785 0,5793 0,0245-0,3795 0,1920 0,1430 0,2784 0,4598 0,2186 233 20 0,0220 0,2471-0,0418-0,2015 0,0810 0,0568 0,2800 0,2939 0,1871 18 21 0,1498 0,3170-0,0729-0,2807 0,0542 0,1104 0,1784 0,4020 0,1996 56 22 0,2191 0,5153 0,0399-0,3071 0,2228 0,3083 0,0651 0,2769 0,2454 382 23 0,1900 0,5920-0,0657-0,3102 0,0897 0,1758 0,0626 0,4371 0,2029 226 24 0,1179 0,4808-0,0615-0,3417 0,1596 0,2762 0,0688 0,3738 0,2645 217 25 0,0975 0,4321-0,0461-0,2930 0,0853 0,2208 0,0659 0,3659 0,2164 106 26 0,0152 0,1780 0,0191-0,0495 0,0226 0,0717-0,0282 0,0240-0,0132 12 27 0,1490 0,3124-0,0272-0,2507 0,1486 0,0878 0,0949 0,3586 0,3126 61 28 0,1544 0,5426-0,0741-0,3367 0,1454 0,1555 0,0172 0,3109 0,2025 86 29 0,2302 0,0821 0,1287-0,1781 0,2422 0,3453 0,0345 0,4070 0,2703 498 30 0,0787 0,0648-0,1023-0,2162-0,1135-0,1245 0,2229 0,2708 0,2004 21 31 0,2158 0,2308-0,1823-0,2933-0,0588-0,0238 0,2268 0,4121 0,1996 99 32 0,1934 0,2161-0,0561-0,2728 0,0174 0,0368 0,1640 0,2546 0,0939 145 33 0,1335 0,1794-0,1092-0,1187-0,3126-0,2756-0,1391-0,0360-0,0416 210 34 0,3087 0,5316-0,1135-0,3062-0,0017-0,0373 0,3417 0,1968 0,2395 331 35 0,1147 0,4083-0,0737-0,3304-0,1029-0,0909 0,4710 0,3218 0,1414 190 36 0,1368 0,4815-0,1140-0,2217 0,0181 0,0358-0,0630 0,1266-0,0843 167 37 0,0228 0,1395-0,1287 0,0224-0,0471 0,0358-0,0159 0,0475-0,0361 115 38 0,2145 0,4724-0,0020-0,1581 0,1397 0,1515 0,1414 0,1927 0,0689 193 39 0,2872 0,5907 0,0547-0,3100 0,2770 0,2890 0,3463 0,3813 0,2526 1596 40 0,0738 0,2746-0,0695-0,2438 0,0910 0,1468 0,1425 0,2367 0,1773 156 41 0,1131 0,2707-0,0069-0,1697 0,0982 0,1475-0,0120 0,2139 0,1667 531 42 0,0757 0,2641 0,0444-0,2558 0,0737 0,1570 0,2219 0,2295 0,1374 625 43 0,0634 0,1921 0,1153-0,1301 0,0487 0,1275 0,2358 0,1265 0,0857 1213

...2 188 44 0,1533 0,3812 45 0,1246 0,4517-0,0632-0,1144 46 0,2172 0,3170 0,0357 є є є 0,1221 0,2960-0,0226-0,2591-0,0506 0,0819 0,1416 0,0473-0,0025 268 - - 0,2477 0,0689 0,0576 0,1368 0,0277 0,0059 414-0,3303 0,2324 0,3479 0,0670 0,1116-0,0147 632-0,2149 0,0592 0,0847 0,1194 0,2120 0,1228 0,3339 0,5920 0,3344 0,6757 0,2770 0,5979 0,5288 0,4598 0,3126 12 8 18 20 15 14 13 9 11.3 - ( 40 ) є є 0-18840 0,0500 0,0070 39,00 10-18260 0,0494 0,0072 39,00 50-15940 0,0465 0,0083 38,95 80-14200 0,0438 0,0093 38,91 100-13040 0,0416 0,0101 38,86 110-12460 0,0403 0,0106 38,85 120-11880 0,0389 0,0111 38,83 190-7820 0,0233 0,0169 38,59 210-6660 0,0153 0,0198 38,46 230-5500 0,0040 0,0240 38,28 235-5210 0,0004 0,0253 38,22 236-5152 -0,0004 0,0256 38,21 240-4920 -0,0037 0,0268 38,16 250-4340 -0,0134 0,0304 38,00 300-1440 -0,1792 0,0917 35,38 320-280 -1,2071 0,4714 19,07 321-222 -1,5405 0,5946 13,78 322-164 -2,1098 0,8049 4,76

...3 189 323-106 -3,3019 1,2453-14,15 324-48 -7,3750 2,7500-78,75 325 10 35,8000-13,2000 606,00 326 68 5,3235-1,9412 122,65 327 126 2,9048-1,0476 84,29 328 184 2,0109-0,7174 70,11 329 242 1,5455-0,5455 62,73 330 300 1,2600-0,4400 58,20 350 1460 0,3137-0,0904 43,19 400 4360 0,1509-0,0303 40,61 500 10160 0,1041-0,0130 39,87 600 15960 0,0914-0,0083 39,67 700 21760 0,0854-0,0061 39,57 800 27560 0,0819-0,0048 39,52 900 33360 0,0797-0,0040 39,48 1000 39160 0,0781-0,0034 39,46 2000 97160 0,0726-0,0014 39,37 3000 155160 0,0713-0,0009 39,35 4000 213160 0,0706-0,0006 39,34 10000 561160 0,0696-0,0002 39,32 20000 1141160 0,0693-0,0001 39,31 30000 1721160 0,0692-0,0001 39,31 50000 2881160 0,0691-0,00005 39,30.4-1-7 є 1 4 58 157 8 161 1 2 6 120 128 5 121 1 3 36 320 1379 126 664 0,6 4 39 601 1274 86 1213 1 5 57 673 2039 191 1773 0,85 6 76 819 2246 278 1812 1 7 89 1123 3242 276 2463 1

.5-8-10 190 LCOM WMC LOC RFC 8 2691 889 21549 2889 61 9 2943 853 19938 2531 86 10 11056 1727 38191 5171 498.6-11-13 LЇC WMC CBЇ RFC 11 28806 1267 2070 4684 21 12 42302 1628 2776 6350 99 13 53500 1951 3265 7559 145.7-14-16 RFC WMC CBЇ CE 14 4514 1806 1904 911 268 15 5660 2312 2460 1163 414 16 8565 3534 3655 1832 632.8-17-19 17 18 19 LOC WMC LCOM NPM 55428 3173 22010 2636 342 119731 5490 41241 4700 496 129327 5971 44405 5127 500

..1-1 1 2 3 4 5 6 1 6 31 7 7 38 7 2 13 34 11 10 1 45 12 3 21 18 13 12 1 31 14 4 30 21 12 9 3 33 15 5 48 21 17 10 7 38 24 6 71 12 22 17 4 1 34 28 7 118 14 24 18 4 2 38 32 8 610 5 38 14 15 4 2 1 2 43 81 156 144 97 35 7 2 1 2 300 213 0,48.2-1 1 2 3 4 5 6 1 6 0,82 0,18 0,18 0,18 2 13 0,76 0,24 0,22 0,02 0,27 3 21 0,58 0,42 0,39 0,03 0,45 4 30 0,64 0,36 0,27 0,09 0,45 5 48 0,55 0,45 0,26 0,18 0,63 6 71 0,35 0,65 0,50 0,12 0,03 0,82 7 118 0,37 0,63 0,47 0,11 0,05 0,84 8 610 0,12 0,88 0,33 0,35 0,09 0,05 0,02 0,05 1,88

192.3-1 1 2 3 4 5 6 1 2 3 4 5 5 1 6 61 13 13 74 13 13 2 13 50 16 15 1 66 15 2 17 3 21 46 34 31 2 80 31 4 35 4 30 50 28 21 7 78 21 14 35 5 48 40 33 19 13 73 19 26 45 6 71 27 50 39 9 2 77 39 18 6 63 7 118 27 46 34 8 4 73 34 16 12 62 8 610 8 56 21 22 6 3 1 3 64 21 44 18 12 5 18 118 309 276 193 62 12 3 1 3 585 193 124 36 12 5 18 388 0,47.4-1 1 2 3 4 5 6 1 2 3 4 5 5 1 6 63 11 8 3 74 8 6 14 2 13 52 14 13 1 66 13 3 16 3 21 45 35 30 5 80 30 10 40 4 30 57 21 13 7 1 78 13 14 4 31 5 48 41 32 21 10 1 73 21 20 3 44 6 71 28 49 38 8 3 77 38 16 9 63 7 118 27 46 28 11 4 2 1 73 28 22 12 8 5 75 8 610 5 59 23 21 7 2 4 2 64 23 42 21 8 20 15 129 318 267 174 65 16 5 5 2 585 174 130 48 20 25 15 412 0,46

193.5-1, %, %, % 1 6 0,18 0,19 5,26 5,26 13,00 14,00 7,14 7,14 17,57 14,86-18,24 18,24 2 13 0,27 0,24-12,50 12,50 17,00 16,00-6,25 6,25 24,24 21,21-14,29 14,29 3 21 0,45 0,50 10,00 10,00 35,00 40,00 12,50 12,50 42,50 43,75 2,86 2,86 4 30 0,45 0,40-12,50 12,50 35,00 31,00-12,90 12,90 35,90 26,92-33,36 33,36 5 48 0,63 0,60-5,00 5,00 45,00 44,00-2,27 2,27 45,21 43,84-3,13 3,13 6 71 0,82 0,82 63,00 63,00 64,94 63,64-2,04 2,04 7 118 0,84 1,03 18,45 18,45 62,00 75,00 17,33 17,33 63,01 63,01 8 610 1,88 2,02 6,93 6,93 118,00 129,00 8,53 8,53 87,50 92,19 5,09 5,09 є 0,69 0,73 1,33 8,83 388,00 412,00 3,01 8,37 47,61 46,18-7,89 9,88-5,48-5,83 3,10-12,50 5,00-12,90 2,27-33,36 2,04 18,45 18,45 17,33 17,33 5,09 33,36 119,63 20,11 101,16 21,60 161,27 115,15.6-2, %, %, % 1 3 1,19 1,52 21,71 21,71 139,00 178,00 21,91 21,91 94,02 98,29 4,34 4,34 2 8 1,00 0,95-5,26 5,26 112,00 106,00-5,66 5,66 94,59 94,59 3 15 1,40 1,31-6,87 6,87 225,00 211,00-6,64 6,64 100,00 99,38-0,62 0,62 4 20 1,00 1,10 9,09 9,09 79,00 87,00 9,20 9,20 100,00 100,00 5 32 1,18 1,14-3,51 3,51 131,00 126,00-3,97 3,97 100,00 99,10-0,91 0,91 6 53 1,27 1,43 11,19 11,19 146,00 164,00 10,98 10,98 100,00 100,00 7 428 1,92 1,85-3,78 3,78 224,00 215,00-4,19 4,19 100,00 100,00 є 1,28 1,33 3,22 8,77 1056,00 1087,00 3,09 8,94 98,37 98,77 0,40 0,84-3,76-2,85-0,40-6,87 3,51-6,64 3,97-0,91 0,62 21,71 21,71 21,91 21,91 4,34 4,34 101,12 34,56 103,98 33,68 5,81 2,85

194.7-3, %, %, % 1 2 0,78 0,88 11,36 11,36 49,00 49,00 53,57 53,57 2 5 0,58 1,10 47,27 47,27 27,00 54,00 50,00 50,00 46,94 63,27 25,81 25,81 3 8 1,31 1,52 13,82 13,82 58,00 73,00 20,55 20,55 68,75 75,00 8,33 8,33 4 13 1,63 1,53-6,54 6,54 95,00 87,00-9,20 9,20 80,70 77,19-4,55 4,55 5 16 2,35 1,65-42,42 42,42 91,33 66,00-38,38 38,38 75,00 72,50-3,45 3,45 6 23 3,48 2,90-20,00 20,00 174,00 145,00-20,00 20,00 80,00 90,00 11,11 11,11 7 37 2,32 2,61 11,11 11,11 85,00 94,00 9,57 9,57 88,89 77,78-14,28 14,28 8 230 10,00 9,75-2,56 2,56 523,00 507,00-3,16 3,16 94,23 92,31-2,08 2,08 є 2,81 2,74 1,51 19,39 1102,33 1075,00 1,17 18,86 73,51 75,20 2,61 8,70 2,55 2,54-2,25-42,42 2,56-38,38 3,16-14,28 2,08 47,27 47,27 50,00 50,00 25,81 25,81 613,57 240,06 709,85 247,18 148,21 58,23.8-4, %, %, % 1 4 0,42 0,65 35,38 35,38 8,00 11,00 27,27 27,27 29,41 47,06 37,51 37,51 2 10 0,81 0,83 2,41 2,41 14,00 15,00 6,67 6,67 44,44 44,44 3 14 1,36 0,86-58,14 58,14 19,00 12,00-58,33 58,33 64,29 57,14-12,51 12,51 4 20 1,14 0,86-32,56 32,56 20,00 18,00-11,11 11,11 61,90 47,62-29,99 29,99 5 27 1,50 1,25-20,00 20,00 29,00 25,00-16,00 16,00 85,00 60,00-41,67 41,67 6 39 2,18 2,00-9,00 9,00 37,00 36,00-2,78 2,78 55,56 66,67 16,66 16,66 7 171 4,81 5,33 9,76 9,76 87,40 96,00 8,96 8,96 83,33 83,33 є 1,75 1,68-10,31 23,89 214,40 213,00-6,47 18,73 60,56 58,04-4,29 19,76 4,17 0,66 4,34-58,14 2,41-58,33 2,78-41,67 12,51 35,38 58,14 27,27 58,33 37,51 41,67 790,08 325,45 623,51 314,56 859,37 129,85

195.9-5, %, %, % 1 20 0,09 0,06-50,00 50,00 30,00 18,00-66,67 66,67 4,93 6,34 22,24 22,24 2 33 0,11 0,10-10,00 10,00 9,00 7,00-28,57 28,57 7,46 10,45 28,61 28,61 3 41 0,14 0,21 33,33 33,33 3,00 5,00 40,00 40,00 8,33 16,67 50,03 50,03 4 48 0,18 0,13-38,46 38,46 6,00 4,00-50,00 50,00 9,68 12,90 24,96 24,96 5 150 0,27 0,34 20,59 20,59 14,00 18,00 22,22 22,22 20,75 30,19 31,27 31,27 є 0,16 0,17-8,91 30,48 62,00 52,00-16,60 41,49 10,23 15,31 31,42 31,42-5,88 19,23-33,18-50,00 10,00-66,67 22,22 22,24 22,24 33,33 50,00 40,00 66,67 50,03 50,03 1043,45 194,02 1695,28 249,39 96,05 96,05.10-6, %, %, % 1 16 0,06 0,08 25,00 25,00 192,00 260,00 26,15 26,15 5,01 6,16 18,67 18,67 2 22 0,07 0,06-16,67 16,67 90,00 73,00-23,29 23,29 6,99 4,78-46,23 46,23 3 30 0,14 0,18 22,22 22,22 114,00 152,00 25,00 25,00 9,97 11,82 15,65 15,65 4 60 0,23 0,23 246,00 240,00-2,50 2,50 19,98 13,55-47,45 47,45 5 309 0,71 0,75 5,33 5,33 369,00 366,00-0,82 0,82 33,06 31,62-4,55 4,55 є 0,24 0,26 7,18 13,84 1011,00 1091,00 4,91 15,55 15,00 13,59-12,78 26,51-7,69-7,33 10,38-16,67 5,33-23,29 0,82-47,45 4,55 25,00 25,00 26,15 26,15 18,67 47,45 275,80 56,79 347,55 129,77 837,20 297,80

196.11-7, %, %, % 1 7 0,07 0,09 22,22 22,22 86,00 126,00 31,75 31,75 4,98 4,28-16,36 16,36 2 10 0,11 0,11 100,00 97,00-3,09 3,09 11,04 9,27-19,09 19,09 3 13 0,12 0,10-20,00 20,00 90,00 78,00-15,38 15,38 12,02 8,71-38,00 38,00 4 19 0,14 0,12-16,67 16,67 207,00 178,00-16,29 16,29 13,97 9,72-43,72 43,72 5 23 0,14 0,14 105,00 97,00-8,25 8,25 14,00 10,14-38,07 38,07 6 28 0,17 0,17 138,00 127,00-8,66 8,66 13,99 12,42-12,64 12,64 7 35 0,20 0,18-11,11 11,11 167,00 131,00-27,48 27,48 15,06 11,80-27,63 27,63 8 355 0,37 0,32-15,63 15,63 439,00 458,00 4,15 4,15 23,01 20,20-13,91 13,91 є 0,17 0,15-5,15 10,70 1332,00 1292,00-5,41 14,38 13,51 10,82-26,18 26,18 13,33 3,10 24,86-20,00 11,11-27,48 3,09-43,72 12,64 22,22 22,22 31,75 31,75-12,64 43,72 240,00 14,57 275,14 97,55 133,93 133,93.12-8, %, %, % 1 5 0,04 0,04 3,00 3,00 3,80 2,53-50,20 50,20 2 17 0,03 0,07 57,14 57,14 4,00 8,00 50,00 50,00 3,31 6,61 49,92 49,92 3 24 0,22 0,23 4,35 4,35 16,00 17,00 5,88 5,88 13,33 14,67 9,13 9,13 4 32 0,11 0,10-10,00 10,00 7,00 7,00 10,45 8,96-16,63 16,63 5 43 0,37 0,37 24,00 24,00 26,15 29,23 10,54 10,54 6 71 0,78 0,75-4,00 4,00 53,00 51,00-3,92 3,92 48,53 45,59-6,45 6,45 7 288 2,22 2,01-10,45 10,45 153,00 139,00-10,07 10,07 65,22 69,57 6,25 6,25 є 0,54 0,51 5,29 12,28 260,00 249,00 5,98 9,98 24,40 25,31 0,37 21,30 5,88 4,42-3,60-10,45 4,00-10,07 3,92-50,20 6,25 57,14 57,14 50,00 50,00 49,92 50,20 646,94 406,39 553,16 357,72 794,68 341,00

197.13-9, %, %, % 1 0 0,31 0,40 22,50 22,50 44,00 56,00 21,43 21,43 26,95 26,24-2,71 2,71 2 1 0,38 0,54 29,63 29,63 25,00 35,00 28,57 28,57 38,46 36,92-4,17 4,17 3 3 0,36 0,52 30,77 30,77 20,00 28,00 28,57 28,57 27,78 31,48 11,75 11,75 4 9 0,91 0,82-10,98 10,98 60,00 53,00-13,21 13,21 35,38 33,85-4,52 4,52 5 30 1,00 0,98-2,04 2,04 64,00 59,00-8,47 8,47 43,33 38,33-13,04 13,04 6 98 1,21 1,44 15,97 15,97 74,00 92,00 19,57 19,57 43,75 42,19-3,70 3,70 7 4361 2,36 1,83-28,96 28,96 132,00 106,00-24,53 24,53 50,00 48,28-3,56 3,56 є 0,82 0,82 7,11 17,61 419,00 429,00 6,49 18,04 33,21 32,16-2,49 5,43 0,00-2,33 3,26-28,96 2,04-24,53 8,47-13,04 2,71 30,77 30,77 28,57 28,57 11,75 13,04 441,01 102,19 419,63 49,43 46,11 15,70.14-10, %, %, % 1 3 0,06 0,10 40,00 40,00 10,00 17,00 41,18 41,18 6,02 8,43 28,59 28,59 2 4 0,25 0,23-8,70 8,70 16,00 16,00 12,68 16,90 24,97 24,97 3 5 0,18 0,19 5,26 5,26 8,00 9,00 11,11 11,11 17,02 17,02 4 7 0,35 0,40 12,50 12,50 45,00 51,00 11,76 11,76 14,84 20,31 26,93 26,93 5 9 0,28 0,35 20,00 20,00 27,00 32,00 15,63 15,63 18,48 17,39-6,27 6,27 6 12 0,53 0,52-1,92 1,92 45,00 44,00-2,27 2,27 23,81 27,38 13,04 13,04 7 19 0,75 0,74-1,35 1,35 72,00 67,00-7,46 7,46 25,27 21,98-14,97 14,97 8 448 2,24 1,91-17,28 17,28 193,00 162,00-19,14 19,14 37,65 38,82 3,01 3,01 є 0,58 0,56 6,06 13,38 416,00 398,00 6,35 13,57 19,47 21,03 9,41 14,72 3,57 4,52-7,42-17,28 1,35-19,14 2,27-14,97 3,01 40,00 40,00 41,18 41,18 28,59 28,59 283,70 141,54 322,88 135,09 256,95 89,56

198.15-11, %, %, % 1 3 0,05 0,05 10,00 10,00 3,74 4,81 22,25 22,25 2 5 0,05 0,08 37,50 37,50 5,00 8,00 37,50 37,50 4,90 7,84 37,50 37,50 3 9 0,12 0,17 29,41 29,41 18,00 34,00 47,06 47,06 4,04 9,60 57,92 57,92 4 12 0,17 0,20 15,00 15,00 18,00 20,00 10,00 10,00 11,00 11,00 5 19 0,25 0,26 3,85 3,85 44,00 47,00 6,38 6,38 18,23 19,89 8,35 8,35 6 22 0,25 0,27 7,41 7,41 19,00 17,00-11,76 11,76 14,06 15,63 10,04 10,04 7 31 0,43 0,54 20,37 20,37 63,00 74,00 14,86 14,86 24,09 25,55 5,71 5,71 8 51 0,57 0,57 90,00 88,00-2,27 2,27 27,27 28,57 4,55 4,55 9 286 1,72 1,51-13,91 13,91 208,50 183,00-13,93 13,93 33,88 33,06-2,48 2,48 є 0,40 0,41 11,07 14,16 475,50 481,00 9,76 15,97 15,69 17,33 15,98 16,53-2,44-1,14-9,46-13,91 3,85-13,93 2,27-2,48 2,48 37,50 37,50 47,06 47,06 57,92 57,92 250,90 121,98 419,43 217,07 362,47 339,79.16-12, %, %, % 1 3 0,33 0,36 8,33 8,33 24,00 26,00 7,69 7,69 33,33 34,72 4,00 4,00 2 8 0,43 0,47 8,51 8,51 55,00 60,00 8,33 8,33 42,64 42,64 3 13 0,44 0,43-2,33 2,33 26,00 25,00-4,00 4,00 41,38 37,93-9,10 9,10 4 17 0,64 0,56-14,29 14,29 38,00 33,00-15,15 15,15 57,63 50,85-13,33 13,33 5 25 0,50 0,47-6,38 6,38 30,00 27,00-11,11 11,11 41,38 43,10 3,99 3,99 6 39 0,67 0,69 2,90 2,90 49,00 51,00 3,92 3,92 50,00 47,30-5,71 5,71 7 57 0,82 0,76-7,89 7,89 64,00 57,00-12,28 12,28 50,67 57,33 11,62 11,62 8 391 1,63 1,56-4,49 4,49 121,00 120,00-0,83 0,83 79,22 71,43-10,91 10,91 є 0,68 0,66-1,96 6,89 407,00 399,00-2,93 7,91 49,53 48,16-2,43 7,33 3,03 2,01 2,84-14,29 2,33-15,15 0,83-13,33 3,99 8,51 14,29 8,33 15,15 11,62 13,33 56,55 12,90 74,46 20,41 75,01 12,50

199.17-13, %, %, % 1 50 0,04 0,04 21,00 23,00 8,70 8,70 3,97 3,78-5,03 5,03 2 85 0,28 0,29 3,45 3,45 19,00 19,00 24,62 23,08-6,67 6,67 3 515 0,55 0,76 27,63 27,63 33,00 48,00 31,25 31,25 25,40 39,68 35,99 35,99 є 0,29 0,36 10,36 10,36 73,00 90,00 13,32 13,32 18,00 22,18 8,10 15,90-19,44-18,89-18,85 3,45 3,45 8,70 8,70-6,67 5,03 27,63 27,63 31,25 31,25 35,99 35,99 146,17 146,17 127,13 127,13 389,47 202,32.18-14, %, %, % 1 9 0,20 0,18-11,11 11,11 9,00 8,00-12,50 12,50 20,00 17,78-12,49 12,49 2 20 0,61 0,87 29,89 29,89 23,00 33,00 30,30 30,30 50,00 63,16 20,84 20,84 3 29 1,24 1,19-4,20 4,20 47,00 44,00-6,82 6,82 70,27 70,27 4 37 1,67 1,53-9,15 9,15 67,00 61,00-9,84 9,84 87,50 77,50-12,90 12,90 5 53 1,11 1,35 17,78 17,78 52,00 62,00 16,13 16,13 56,52 65,22 13,34 13,34 6 76 1,29 1,68 23,21 23,21 54,00 69,00 21,74 21,74 63,41 90,24 29,73 29,73 7 580 2,06 2,55 19,22 19,22 74,00 97,00 23,71 23,71 76,32 76,32 є 1,17 1,34 9,38 16,37 326,00 374,00 8,96 17,29 60,57 65,78 5,50 12,76-12,69-12,83-7,92-11,11 4,20-12,50 6,82-12,90 12,49 29,89 29,89 30,30 30,30 29,73 29,73 246,70 66,80 278,67 59,96 304,36 44,73

200.19-15, %, %, % 1 10 0,63 0,60-5,00 5,00 33,00 31,00-6,45 6,45 34,62 32,69-5,90 5,90 2 15 1,63 1,44-13,19 13,19 26,00 26,00 61,11 66,67 8,34 8,34 3 23 1,15 1,24 7,26 7,26 16,00 21,00 23,81 23,81 47,06 47,06 4 33 1,30 1,36 4,41 4,41 56,00 53,00-5,66 5,66 56,41 61,54 8,34 8,34 5 47 2,18 1,83-19,13 19,13 37,00 33,00-12,12 12,12 94,44 66,67-41,65 41,65 6 150 2,83 2,59-9,27 9,27 138,00 119,00-15,97 15,97 82,61 67,39-22,58 22,58 7 839 9,00 9,20 2,17 2,17 94,00 92,00-2,17 2,17 100,00 100,00 є 2,67 2,61-4,68 8,63 400,00 375,00-2,65 9,45 68,04 63,15-7,64 12,40 2,30 6,67 7,74-19,13 2,17-15,97 2,17-41,65 5,90 7,26 19,13 23,81 23,81 8,34 41,65 82,07 29,44 164,96 52,87 369,42 182,26

.1-11 ------------------------------------------------------------------------------------------------------ ** 11 ** є ** є tabliza1(m+2,12) 1 12 nazva=tabliza1 select 1 set filter to np > partp go top ** ********************* 1- m=1 count for cm<=inter(m,2).and. bug=0 to &nazva(m,3) count for cm<=inter(m,2).and. bug#0 to &nazva(m,4) count for cm<=inter(m,2).and. bug=1 to &nazva(m,5) count for cm<=inter(m,2).and. bug=2 to &nazva(m,6) count for cm<=inter(m,2).and. bug=3 to &nazva(m,7) count for cm<=inter(m,2).and. bug=4 to &nazva(m,8) count for cm<=inter(m,2).and. bug=5 to &nazva(m,9) count for cm<=inter(m,2).and. bug>5 to &nazva(m,10) count for cm<=inter(m,2) to &nazva(m,11) sum bug for cm<=inter(m,2) to &nazva(m,12) ************************ m=2 do while m<=kolzap-1 select 1 count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=0 to &nazva(m,3) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug#0 to &nazva(m,4) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=1 to &nazva(m,5) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=2 to &nazva(m,6) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=3 to &nazva(m,7) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=4 to &nazva(m,8) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=5 to &nazva(m,9) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug>5 to &nazva(m,10) count for cm>inter(m-1,2).and. cm<=inter(m,2) to &nazva(m,11) sum bug for cm>inter(m-1,2).and. cm<=inter(m,2) to &nazva(m,12) m=m+1 enddo ********************** ********************** m=kolzap select 1 count for cm>inter(m-1,2).and. bug=0 to &nazva(m,3) count for cm>inter(m-1,2).and. bug#0 to &nazva(m,4) count for cm>inter(m-1,2).and. bug=1 to &nazva(m,5) count for cm>inter(m-1,2).and. bug=2 to &nazva(m,6) count for cm>inter(m-1,2).and. bug=3 to &nazva(m,7)

count for cm>inter(m-1,2).and. bug=4 to &nazva(m,8) count for cm>inter(m-1,2).and. bug=5 to &nazva(m,9) count for cm>inter(m-1,2).and. bug>5 to &nazva(m,10) count for cm>inter(m-1,2) to &nazva(m,11) sum bug for cm>inter(m-1,2) to &nazva(m,12) *********************** 1- m=1 && n=1 && 12 do while m<=12 do while n<=kolzap &nazva(kolzap+1,m)=&nazva(kolzap+1,m)+&nazva(n,m) n=n+1 enddo m=m+1 n=1 enddo *********************** if &nazva(kolzap+1,11)#0 &nazva(kolzap+2,4)=round( &nazva(kolzap+1,4) / &nazva(kolzap+1,11), 2) endif ************************************************************************ return ------------------------------------------------------------------------------------------------------.2-12 ------------------------------------------------------------------------------------------------------ ** 12 ** є ** є tabliza2(m,11) 2 11 m=1 do while m<=kolzap if tabliza1(m,11)#0 tabliza2(m,3) = round(tabliza1(m,3) /tabliza1(m,11), 2) &&. tabliza2(m,4) = round(tabliza1(m,4) /tabliza1(m,11), 2) &&. tabliza2(m,5) = round(tabliza1(m,5) /tabliza1(m,11), 2) && 1-. tabliza2(m,6) = round(tabliza1(m,6) /tabliza1(m,11), 2) && 2- tabliza2(m,7) = round(tabliza1(m,7) /tabliza1(m,11), 2) && 3- tabliza2(m,8) = round(tabliza1(m,8) /tabliza1(m,11), 2) && 4- tabliza2(m,9) = round(tabliza1(m,9) /tabliza1(m,11), 2) && 5- tabliza2(m,10)= round(tabliza1(m,10)/tabliza1(m,11), 2) && 6-. tabliza2(m,11) = round( tabliza1(m,12)/tabliza1(m,11), 2) && endif && m=m+1 enddo return ------------------------------------------------------------------------------------------------------.3-13 ------------------------------------------------------------------------------------------------------ ** 13 ** є 202

** є tabliza3(m+2,18) 3 18 select 1 set filter to np < partp go top m=1 count for cm<=inter(1,2) to tabliza3(1,11) ****************************************** go top m=2 do while m<=kolzap-1 count for cm>inter(m-1,2).and. cm<=inter(m,2) to tabliza3(m,11) m=m+1 enddo ****************************** m=kolzap ******************************** count for cm>inter(m-1,2) to tabliza3(m,11) ************************************************************************ m=1 do while m<=kolzap tabliza3(m,3)=round(tabliza2(m,3) *tabliza3(m,11), 0) && tabliza3(m,4)=round(tabliza2(m,4) *tabliza3(m,11), 0) && tabliza3(m,5)=round(tabliza2(m,5) *tabliza3(m,11), 0) && 1 tabliza3(m,6)=round(tabliza2(m,6) *tabliza3(m,11), 0) && 2 tabliza3(m,7)=round(tabliza2(m,7) *tabliza3(m,11), 0) && 3 tabliza3(m,8)=round(tabliza2(m,8) *tabliza3(m,11), 0) && 4 tabliza3(m,9)=round(tabliza2(m,9) *tabliza3(m,11), 0) && 5 tabliza3(m,10)=round(tabliza2(m,10)*tabliza3(m,11), 0) && 6. ** tabliza3(m,11) tabliza3(m,12)=tabliza3(m,5) && 1 tabliza3(m,13)=tabliza3(m,6)*2 && 2 tabliza3(m,14)=tabliza3(m,7)*3 && 3 tabliza3(m,15)=tabliza3(m,8)*4 && 4 tabliza3(m,16)=tabliza3(m,9)*5 && 5 ****************** є 6 if tabliza1(m,10)#0 && 6 d6=tabliza1(m,12)-tabliza1(m,5) *1 ; -tabliza1(m,6) *2 ; -tabliza1(m,7) *3 ; -tabliza1(m,8) *4 ; -tabliza1(m,9) *5 tabliza3(m,17)=round( tabliza3(m,10)* d6/tabliza1(m,10), 2) endif ************************** є tabliza3(m,18)=tabliza3(m,12)+tabliza3(m,13)+tabliza3(m,14)+ ; tabliza3(m,15)+tabliza3(m,16)+tabliza3(m,17) m=m+1 n=1 enddo ************************** 3 m=3 n=1 do while m<=18 do while n<=kolzap 203

tabliza3(kolzap+1,m)=tabliza3(kolzap+1,m)+tabliza3(n,m) 204 n=n+1 enddo m=m+1 n=1 enddo *********************** є tabliza3(kolzap+2,4)=round( tabliza3(kolzap+1,4) / tabliza3(kolzap+1,11), 2) ************************************************************************ return ------------------------------------------------------------------------------------------------------.4-16 ------------------------------------------------------------------------------------------------------ ** 16 ** є ( ) ** є tabliza4(m+2,18) 4 18 select 1 set filter to np < partp go top m=1 count for cm<=inter(m,2).and. bug=0 to &nazva(m,3) count for cm<=inter(m,2).and. bug#0 to &nazva(m,4) count for cm<=inter(m,2).and. bug=1 to &nazva(m,5) count for cm<=inter(m,2).and. bug=2 to &nazva(m,6) count for cm<=inter(m,2).and. bug=3 to &nazva(m,7) count for cm<=inter(m,2).and. bug=4 to &nazva(m,8) count for cm<=inter(m,2).and. bug=5 to &nazva(m,9) count for cm<=inter(m,2).and. bug>5 to &nazva(m,10) count for cm<=inter(m,2) to &nazva(m,11) sum bug for cm<=inter(m,2).and. bug=1 to &nazva(m,12) sum bug for cm<=inter(m,2).and. bug=2 to &nazva(m,13) sum bug for cm<=inter(m,2).and. bug=3 to &nazva(m,14) sum bug for cm<=inter(m,2).and. bug=4 to &nazva(m,15) sum bug for cm<=inter(m,2).and. bug=5 to &nazva(m,16) sum bug for cm<=inter(m,2).and. bug>5 to &nazva(m,17) sum bug for cm<=inter(m,2) to &nazva(m,18) ************************************************************************* m=2 do while m<=kolzap-1 count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=0 to &nazva(m,3) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug#0 to &nazva(m,4) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=1 to &nazva(m,5) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=2 to &nazva(m,6) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=3 to &nazva(m,7) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=4 to &nazva(m,8) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=5 to &nazva(m,9) count for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug>5 to &nazva(m,10) count for cm>inter(m-1,2).and. cm<=inter(m,2) to &nazva(m,11) sum bug for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=1 to &nazva(m,12) sum bug for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=2 to &nazva(m,13)

sum bug for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=3 to &nazva(m,14) sum bug for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=4 to &nazva(m,15) sum bug for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug=5 to &nazva(m,16) sum bug for cm>inter(m-1,2).and. cm<=inter(m,2).and. bug>5 to &nazva(m,17) sum bug for cm>inter(m-1,2).and. cm<=inter(m,2) to &nazva(m,18) m=m+1 enddo ****************************************** m=kolzap********************** count for cm>inter(m-1,2).and. bug=0 to &nazva(m,3) count for cm>inter(m-1,2).and. bug#0 to &nazva(m,4) count for cm>inter(m-1,2).and. bug=1 to &nazva(m,5) count for cm>inter(m-1,2).and. bug=2 to &nazva(m,6) count for cm>inter(m-1,2).and. bug=3 to &nazva(m,7) count for cm>inter(m-1,2).and. bug=4 to &nazva(m,8) count for cm>inter(m-1,2).and. bug=5 to &nazva(m,9) count for cm>inter(m-1,2).and. bug>5 to &nazva(m,10) count for cm>inter(m-1,2) to &nazva(m,11) sum bug for cm>inter(m-1,2).and. bug=1 to &nazva(m,12) sum bug for cm>inter(m-1,2).and. bug=2 to &nazva(m,13) sum bug for cm>inter(m-1,2).and. bug=3 to &nazva(m,14) sum bug for cm>inter(m-1,2).and. bug=4 to &nazva(m,15) sum bug for cm>inter(m-1,2).and. bug=5 to &nazva(m,16) sum bug for cm>inter(m-1,2).and. bug>5 to &nazva(m,17) sum bug for cm>inter(m-1,2) to &nazva(m,18) **************************** 4 m=1 n=1 do while m<=18 do while n<=kolzap &nazva(kolzap+1,m)=&nazva(kolzap+1,m)+&nazva(n,m) n=n+1 enddo m=m+1 n=1 enddo *********************** tabliza4(kolzap+2,4)=round( tabliza4(kolzap+1,4) / tabliza4(kolzap+1,11), 2) ************************************************************************ return ------------------------------------------------------------------------------------------------------.5-17 ------------------------------------------------------------------------------------------------------ ** 17 ** є ( ) ** є tabliza5(m+2,14) 5 14 itog3=0 && itog4=0 && itog5=0 && itog6=0 && itog7=0 && 205

itog8=0 && itog9=0 && itog10=0 && itog11=0 && itog12=0 && itog13=0 && itog14=0 && ************************************************************************ m=1 do while m<=kolzap tabliza5(m,3) = tabliza2(m,11) if tabliza4(m,11) #0 tabliza5(m,4) = round(tabliza4(m,18) /tabliza4(m,11), 2) endif if tabliza5(m,4)#0 tabliza5(m,5) = round( (tabliza5(m,4)-tabliza5(m,3))/tabliza5(m,4)*100, 2) endif if tabliza5(m,4)#0 tabliza5(m,6) = abs( round( (tabliza5(m,4)-tabliza5(m,3))/tabliza5(m,4)*100, 2) ) endif itog3=itog3+tabliza5(m,3) itog4=itog4+tabliza5(m,4) itog5=itog5+tabliza5(m,5) itog6=itog6+tabliza5(m,6) ************************************************************************** tabliza5(m,7) = tabliza3(m,18) tabliza5(m,8) = tabliza4(m,18) if tabliza5(m,8)#0 tabliza5(m,9) = round( (tabliza5(m,8)-tabliza5(m,7))/tabliza5(m,8)*100, 2) endif if tabliza5(m,8)#0 tabliza5(m,10)= abs( round( (tabliza5(m,8)-tabliza5(m,7))/tabliza5(m,8)*100, 2) ) endif itog7 =itog7 +tabliza3(m,18) itog8 =itog8 +tabliza4(m,18) itog9 =itog9 +tabliza5(m,9) itog10=itog10+tabliza5(m,10) ************************************************************************** if tabliza3(m,11)#0 tabliza5(m,11) = round( tabliza3(m,4) / tabliza3(m,11)*100, 2) endif if tabliza4(m,11)#0 tabliza5(m,12) = round( tabliza4(m,4) / tabliza4(m,11)*100, 2) endif if tabliza5(m,12)#0 tabliza5(m,13) = round( (tabliza5(m,12)-tabliza5(m,11))/tabliza5(m,12)*100, 2) endif if tabliza5(m,12)#0 tabliza5(m,14)= abs( round( (tabliza5(m,12)-tabliza5(m,11))/tabliza5(m,12)*100, 2) ) 206

endif 207 itog11=itog11+tabliza5(m,11) itog12=itog12+tabliza5(m,12) itog13=itog13+tabliza5(m,13) itog14=itog14+tabliza5(m,14) ************************************************************************ m=m+1 enddo ************************************************************************ tabliza5(kolzap+1,3) = round( itog3/kolzap, 2) tabliza5(kolzap+1,4) = round( itog4/kolzap, 2) tabliza5(kolzap+1,5) = round( itog5/kolzap, 2) tabliza5(kolzap+1,6) = round( itog6/kolzap, 2) if tabliza5(kolzap+1,4)#0 tabliza5(kolzap+2,4) = round( (tabliza5(kolzap+1,3)-tabliza5(kolzap+1,4)) ; / tabliza5(kolzap+1,4)*100, 2) endif ************************************************************************ tabliza5(kolzap+1,7) = itog7 tabliza5(kolzap+1,8) = itog8 tabliza5(kolzap+1,9) = round( itog9 /kolzap, 2) tabliza5(kolzap+1,10)= round( itog10/kolzap, 2) if tabliza5(kolzap+1,8)#0 tabliza5(kolzap+2,8) = round( (tabliza5(kolzap+1,7)-tabliza5(kolzap+1,8)) ; / tabliza5(kolzap+1,8)*100, 2) endif ************************************************************************ tabliza5(kolzap+1,11) = round( itog11/kolzap, 2) tabliza5(kolzap+1,12) = round( itog12/kolzap, 2) tabliza5(kolzap+1,13) = round( itog13/kolzap, 2) tabliza5(kolzap+1,14) = round( itog14/kolzap, 2) if tabliza5(kolzap+1,12)#0 tabliza5(kolzap+2,12) = round( (tabliza5(kolzap+1,11)-tabliza5(kolzap+1,12)) / tabliza5(kolzap+1,12)*100, 2) endif ************************************************************************ return ------------------------------------------------------------------------------------------------------

.6-208

.7 - є 209

.8 - є 210