1. Prediction Basic concepts

2. Scope Prediction of:  Resources  Calendar time  Quality (or lack of quality)  Change impact  Process performance  Often confounded with the decision process

3. Historical data Y (dependent, observed, response variable) X (independent, prediction variable) known unknown x0x0 prediction interval of new observation Y 0 at x 0 explained variance of observed Y i

4. Methods for building prediction models  Statistical  Parametric  Make assumptions about distribution of the variables  Good tools for automation  Linear regression, Variance analysis,...  Non-parametric, robust  No assumptions about distribution  Less powerful, low degree of automation  Rank-sum methods, Pareto diagrams,...  Causal models  Link elements with semantic links or numerical equations  Simulation models, connectionism models, genetic models,...  Judgemental  Organise human expertise  Delphi method, pair-wise comparison, rule-based methods

5. Common SE-predictions  Detecting fault-prone modules  Project effort estimation  Change Impact Analysis  Ripple effect analysis  Process improvement models  Model checking  Consistency checking

6. Introduction  There are many faults in software  Faults are costly to find and repair  The later we find faults the more costly they are  We want to find faults early  We want to have automated ways of finding faults  Our approach  Automatic measurements on models  Use metrics to predict fault-prone modules

7. Related work  Niclas Ohlsson, PhD work 1993  AXE, fault prediction, introduced Pareto diagrams,  Predictor: number of new and changed signals  Lionel Briand, Khaled El Eman, et al  Numerous contributions in exploring relations between fault- proness and object-oriented metrics  Piotr Tomaszewski, PhD Karlskrona 2006  Studies fault density  Comparison of statistical methods and expert judgement  Jeanette Heidenberg, Andreas Nåls  Discover weak design and propose changes

8. Approach  Find metrics (independent variables)  Number of model elements (size)  Number of changed methods (change)  Transitions per state (complexity)  Changed operations * transitions per state (combinations) ...  Use metrics to predict (dependent variable)  Number of TRs

9. Capsules

10. State charts

11. package capsuleclass attributeoperationportprotocol signal State machine Statetransition Data model

12. Our project - modelmet  RNC application - Three releases  About 7000 model elements  TR statistics database (2000 TRs)  Find metrics  Existing metrics (done at standard daily build)  Run scripts on models  Statistical analysis  Linear regression, principal component analysis, discriminant analysis, robust methods  Neural networks, Bayesian belief networks

13. Size Change Complexity Combined

14. Metrics based on change, system A

15. Metrics based on change, system B

16. Complexity and size metrics, system A

17. Complexity and Size metrics, system B

18. Other metrics, system A TRD = C + 0.034 states – 0.965 protocols modelelements

19. Other metrics, system B

21. How to use predictions  Uneven distribution of faults is common – 80/20 rule  Perform special treatment on selected parts  Select experienced designers  Provide good working conditions  Parallell teams  Inspections  Static and dynamic analysis tools ...  Perform root-cause analysis and make corrections

22. Results Contributions:  Valid statistical material:  Large models, large number of TRs  Two change projects  Two highly explanatory predictors were found  State chart metrics are as good as OO metrics Problems:  Some problems to match modules in models and TRs  Effort to collect change data