Module Size Distribution and Defect Density

Transcription

1 Module Size Ditribution and Defect Yahwant K. Malaiya and Jaon Denton Computer Science Dept. Colorado State Univerity 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 Defect A Compoite Defect Model Module related defect denity : total a Dm ( ) Intruction related defect denity D ( ) i D( ) defect/module : b D a m c ( ) b c D ( ) i a, The compoite defect denity i module ize : then compoite D D i Module Size 6

7 The Two Region The Model minimum defect denity occur at module ize Region Region implie min two region A : For module B: For module : D with min a c with (2 min min ac b) A B

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

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

10 Defect denity Columbu Data Oberved Fitted Module ize 10

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

12 Module Count Module count Ditribution of Module Size function for module ize ditribution : f ( ) g. e g Module Size 100 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 Overall Defect : total project ize, M : number of module, the overall defect denity i max Example : M 400, g 0.004, larget module 2000 line, a 120, b 1.8,c D S S min T T max 1 Mge Mge g g a (.. d D 7.09 per KLOC b c).10 S T 3.. d 100,000 line 14

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

16 Sub-model: Module Size Ditribution Multiplicative ub - model Default value :1 Parameter etimated uing calibration Auming exponentia l ditribution 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 into account variation in ize ditribution. Adjuting ize ditribution may minimize defect. Impact of merging or breaking module need to be tudied. 18

19 Roenberg Analyi Aume X and Y are tatitically independent. Scatter-plot of Y/X againt X look like declining defect denity v. module ize plot (Region A). But: 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

20 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. 20

21 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,

22 Fenton & Ohlon Fenton, N., Ohlon, N.: Quantitative analyi of fault and failure in a complex oftware ytem. IEEE Tranaction on Software Engineering, (2000) 22