Module Size Distribution and Defect Density

Transcription

1 Module Size Ditribution and Defect Computer Science Dept. Colorado State Univerity With update 1

2 Module Size Ditribution and Defect Significance: Factor that affect defect denity Exiting work: Data & Hypothei A New Compoite Defect Model Available Data & Model Module Size Ditribution: Predictable? Total Defect Content & Implication Obervation & Concluion 2

3 Factor Affecting Defect Multiplicative model: RADC ROBUST Sub-model: Phae Programming team Proce maturity Structure Requirement volatility 3

4 Earlier Studie Shen et al.: For module > 500 line, no ize-denity relation. Smaller module: denity decline with ize Banker and Kemerer: Hypothei for optimal module ize Withrow: minimum near ize 200 Hatton: two eparate model for maller & larger module Roenberg: module ize-defect denity correlation mileading Fenton and Ohlon: no ignificant dependence oberved 4

5 A Compoite Defect Model Module-related fault: aociated with parameter paed among the module, aumption made by module regarding each other, handling of global data, Aumption: uch fault are uniformly ditributed among the module. Intruction-related fault: bulk defect denity. Aumption: defect denity component are contant, number of other intruction a given intruction may interact with. 5

6 A Compoite Defect Model Module D The total m ( ) Intructio D ( ) i D( ) related defect/mo a n related b D a compoite m c ( ) b defect defect dule D ( ) c defect i denity : a, module denity : denity i then ize Defect : compoite D D i Module Size 6

7 The Two Region The minimumdefect denity occurat moduleize min Modelimplietwo region: min RegionA : For modulewith RegionB : For modulewith D a c (2 min min ac b) A B

8 Data: Baili & Perricone Module Size (max) Module count Cyclomatic Complexity Defect (/KLOC) Defect Oberved Fitted Module Size 8

9 Withrow Data Source Line Module Defect Defect denity 4 2 Oberved Fitted Module ize 9

10 Columbu Data Defect denity Oberved Fitted Module ize 10

11 Parameter Value Data S min Parameter Value a b c Baili NA Columbu Withrow

12 Ditribution of Module Size function for module ize ditribution : f ( ) g. e g Module count Module Count Module Size Gnu C Library Module ize Baili Data 12

13 Module Size Ditribution: Parameter Data Language M (total module) Parameter g Baili Fortran Withrow ADA Shen PL/S Gnu C Lib C

14 If the D Overall Defect overall Example 2000 line, S S D min T T : total max max project defect Mge : M a 120, b 1.8, c Mge per g g denity ( a 400,.. d ize, g KLOC M : number i b c).10 S T 0.004, 100, d larget of line module, module 14

15 Optimal Module Size Ditribution? If If D all they hence Merge module max 1 are g g maller peak near 2 opt can exponentia e g 0.001( ag min ( b 2 2c and a module. a be equal, lly b c).10 c g ) make ditribute 3.. d them d : a opt 2c reulting in a min 2 min. 15

16 Sub-model: Module Size Ditribution Multiplica tive ub - model Default va lue :1 Parameter etimated uing calibratio n Auming exponentia l ditributi on F m Ag B C g Example : If a 120, b 1.8, c 0.006, and default g F m 25g g 3 16

17 Obervation on Data Trend not obervable if number of module i mall. Trend for region B not oberved if ize< min for mot module, a in Baili & Perricone data (very few module >400). Weak dependence. Trend for region A not oberved if ize> min for mot module, a in Fenton and Ohlon data (very few module with ize <500). Stronger dependence. Selective teting or uneven reue may mak dependence. Avoiding very mall module may be more beneficial than avoiding very large module. 17

18 Concluion A model explaining both declining and riing defect denity trend. Module ize ditribution i often exponential due to natural reaon. A defect denity model to take variation in ize ditribution into account. Adjuting ize ditribution may minimize defect. Impact of merging or breaking module need to be tudied. 18

19 Recent Development 19

20 Roenberg Analyi Argument: If we aume X and Y are tatitically independent. Then catter-plot of Y/X againt X look like declining defect denity v. module ize plot (Region A). Flaw: Note that aumption implie that total defect in a module i independent of module ize, i.e. defect denity i inverely proportional to module ize. J. Roenberg, Some miconception about line of code, Proc. Int. Software Metric Symp, pp , Nov

21 A tudent wrote in 2011 I almot caued a riot at work when I mentioned that there wa data howing that larger oftware module had a lower defect denity that more maller module. 21

22 AT&T Study Thoma J. Otrand and Elaine J. Weyuker The ditribution of fault in a large indutrial oftware ytem. In Proceeding of the 2002 ACM SIGSOFT international ympoium on Software teting and analyi (ISSTA '02). ACM, New York, NY, USA,

23 Fenton & Ohlon: no module < 500 Fenton, N., Ohlon, N.: Quantitative analyi of fault and failure in a complex oftware ytem. IEEE Tranaction on Software Engineering, (2000) 23

24 Koru et al. In Mozilla and Eclipe, an inpection trategy inveting 80 percent of available reource on 100-LOC clae and the ret on 1,000-LOC clae would be more than twice a cot-effective a the oppoite trategy. We oberved that defect pronene increaed with module ize but at a maller rate. Therefore, maller module were proportionally more defect prone compared to larger one. A. G.; Koru, D. Zhang, K. El Emam, Hongfang Liu, "An Invetigation into the Functional Form of the Size-Defect Relationhip for Software Module," IEEE Tran. on Software Eng., vol. 35, no. 2, pp , March/April,