1. Software Metrics and Defect Prediction Ayşe Başar Bener

2. Problem 1 How to tell if the project is on schedule and within budget? Earned-value charts.

3. Problem 2 How hard will it be for another organization to maintain this software? McCabe Complexity

4. Problem 3 How to tell when the subsystems are ready to be integrated Defect Density Metrics.

5. Problem Definition Software development lifecycle: Requirements Design Development Test (Takes ~50% of overall time) Detect and correct defects before delivering software. Test strategies: Expert judgment Manual code reviews Oracles/ Predictors as secondary tools

6. Testing

7. Defect Prediction 2-Class Classification Problem. Non-defective If error = 0 Defective If error > 0 2 things needed: Raw data: Source code Software Metrics -> Static Code Attributes

9. Static Code Attributes void main() { //This is a sample code //Declare variables int a, b, c; // Initialize variables a=2; b=5; //Find the sum and display c if greater than zero c=sum(a,b); if c < 0 printf(“%d\n”, a); return; } int sum(int a, int b) { // Returns the sum of two numbers return a+b; } c > 0 c ModuleLOCLOCCVCCError main()164522 sum()51310 LOC: Line of Code LOCC: Line of commented Code V: Number of unique operands&operators CC: Cyclometric Complexity

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13. Research on Defect Prediction Defect prediction using machine learning techniques How effectively we can estimate defect density? Regression models First classification, then regression Defect prediction in multi version software Defect prediction in embedded software B. Turhan, and A. Bener, "A Multivariate Analysis of Static Code Attributes for Defect Prediction", QSIC 2007, Portland, USA, October 11-12, 2007 A.D. Oral and A. Bener, "Defect Prediction for Embedded Software", ISCIS 2007, Ankara, Turkey, November 9-11, 2007. Software Defect Identification Using Machine Learning Techniques”, E. Ceylan, O. Kutlubay, A. Bener, EUROMICRO SEAA, Dubrovnik, Croatia, August 28th - September 1st, 2006 "Mining Software Data", B. Turhan and O. Kutlubay, Data Mining and Business Intelligence Workshop in ICDE'07, İstanbul, April 2007 "A Two-Step Model for Defect Density Estimation", O. Kutlubay, B. Turhan and A. Bener, EUROMICRO SEAA, Lübeck, Germany, August 2007 "A Defect Prediction Method for Software Versioning", Y. Kastro and A. Bener, Software Quality Journal (in print). “Software Defect Density Estimation Using Static Code Attributes: A Two Step Model”, O. Kutlubay, B. Turhan, A. Bener, Eng. App. of AI (under review)

14. Constructing Predictors Baseline: Naive Bayes. Why?: Best reported results so far (Menzies et al., 2007) Remove assumptions and construct different models. Independent Attributes ->Multivariate dist. Attributes of equal importance "Software Defect Prediction: Heuristics for Weighted Naïve Bayes", B. Turhan and A. Bener, ICSOFT2007, Barcelona, Spain, July 2007. “Software Defect Prediction Modeling”, B. Turhan, IDOESE 2007, Madrid, Spain, September 2007 “Yazılım Hata Kestirimi için Kaynak Kod Ölçütlerine Dayalı Bayes Sınıflandırması”, UYMS2007, Ankara, September 2007 “A Multivariate Analysis of Static Code Attributes for Defect Prediction”, B. Turhan and A. Bener QSIC 2007, Portland, USA, October 2007.

15. Weighted Naive Bayes Naive Bayes Weighted Naive Bayes

16. Datasets Name# Features#ModulesDefect Rate(%) CM1 38 5059 PC1 38 11076 PC2 38 55890.6 PC3 38 156310 PC4 38 145812 KC3 38 4589 KC4 38 1254040 MW1 38 4039

17. Performance Measures Defects Actual noyes Prd no AB yes CD Accuracy: (A+D)/(A+B+C+D) Pd (Hit Rate): D / (B+D) Pf (False Alarm Rate): C / (A+C)

19. Results: InfoGain&GainRatio Data WNB+IG (%)WNB+GR (%)IG+NB (%) pdpdpfbalpdpfbalpdpfbal CM1823970823970833274 PC1693567693567401257 PC2721577662072 1577 PC3803571813572601570 PC4882779872481922978 KC3802776833076481562 KC4773570783571793372 MW1703866683467440760 Avg:773172773272652061

20. Results: Weight Assignments

21. ICSOFT’07 WC vs CC Data? When to use WC or CC? How much data do we need to construct a model?

22. ICSOFT’07

24. Thank You http://softlab.boun.edu.tr