A defect prediction model based on the relationships between developers and changed files
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The document proposes a new defect prediction model called the Developer Based Changes Model (DBCM) which is based on relationships between developers and changed files. It evaluates DBCM on several open source projects and finds it achieves similar accuracy to an existing approach based on number of changes. The document also combines DBCM and the existing approach, finding the combined model further improves prediction accuracy by 5% over the individual models.
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A defect prediction model based on the relationships between developers and changed files
- 1. A DEFECT PREDICTION MODEL BASED ON THE RELATIONSHIPS BETWEEN DEVELOPERS AND CHANGED FILES University of Salerno 26th September 2014 Candidate: Dario Di Nucci Advisor: Andrea De Lucia
- 2. Outline 1. Motivation 2. Related work 3. A new metric for defect prediction based on the relationships between developers and files 4. Case study: empirical evaluation of the prediction model 5. Case study: combination of prediction models 6. Conclusion and future works
- 3. Software Evolution and Defect Prediction
- 5. 50% of development costs B. Beizer. - Software Testing Techniques (2nd ed.). Van Nostrand Reinhold Co., New York, NY, USA, 1990
- 6. G. Myers - The Art of Software Testing Wiley. ISBN 978-0-471-46912-4 (2004)
- 7. Knowing which software components contain defects could be crucial
- 9. There are a lot of prediction metrics Based on the state of the project V. R. Basili, L. C. Briand, and W. L. Melo A validation of object-oriented design metrics as quality indicators IEEE Transactions on Software Engineering, 22 (10): 751 – 761, 1996. Based on the history of the project A. Bernstein, J. Ekanayake, and M. Pinzger “Improving defect prediction using temporal features and non linear models,” Proceedings of IWPSE 2007, 2007, pp. 11–18.
- 10. There are a lot of prediction metrics No one considers developers! V. R. Basili, L. C. Briand, and W. L. Melo A validation of object-oriented design metrics as quality indicators IEEE Transactions on Software Engineering, 22 (10): 751 – 761, 1996. A. Bernstein, J. Ekanayake, and M. Pinzger “Improving defect prediction using temporal features and non linear models,” Proceedings of IWPSE 2007, 2007, pp. 11–18.
- 11. Also the best developer could not make a good job when working on different tasks at the same time
- 12. Introducing Developer Based Changes Model A new model for defect prediction based on the relationships between developers and files
- 14. Two metrics for analyzing the developer confusion Developer Structural Confusion Developer Semantical Confusion
- 15. A new metric for file complexity based on developers confusion Structural File Complexity Semantical File Complexity
- 16. DBCM Process
- 17. DBCM Process
- 18. DBCM Process
- 19. DBCM Process
- 20. DBCM Process
- 21. DBCM Process
- 22. Classifier training & test set The system evolution processes are self-regulating with the distribution of product and process measures close to normal Lehman, M. M., “On Understanding Laws, Evolution, and Conservation in the Large- Program Life Cycle” - Journal of Systems and Software (1980)
- 23. Classifier training & test set How long should be a period?
- 24. Classifier training & test set How long should be a period? 3 Months Ahmed E. Hassan Predicting faults using the complexity of code changes. ICSE 2009: 78 - 88
- 25. Classifier training & test set Which classifier to use?
- 26. Classifier training & test set Decision Table Majority Which classifier to use?
- 28. Case study design Apache Ant Apache Cassandra Apache JMeter Apache Lenya Apache Log4j Apache Poi Apache Tomcat 7 Apache Xerces-J Even if the systems are of the same ecosystem, they have different size and different history
- 29. Case study design RQ1: What is the accuracy of the prediction made by DBCM? RQ2: How does DBCM compared with techniques based on number of changes?
- 31. Case study design We selected as competitive approach the BCCM proposed by Hassan Higher the number of changes applied to a component, higher is the probability that the component is buggy Ahmed E. Hassan Predicting faults using the complexity of code changes. ICSE 2009: 78 - 88
- 32. Results DBCM and BCCM seems to capture the same phenomenal
- 34. DBCM & BCCM: A Combined Approach RQ3: Is it possible to combine the two approaches in order to increase the prediction accuracy?
- 35. DBCM & BCCM: A Combined Approach RQ3: Is it possible to combine the two approaches in order to increase the prediction accuracy? “Big Bang” Combination
- 36. DBCM & BCCM: A Combined Approach RQ3: Is it possible to combine the two approaches in order to increase the prediction accuracy? “Big Bang” Combination Selection Algorithm
- 37. DBCM & BCCM: A Combined Approach Step 1: Identification of the characteristics of the periods influencing the accuracy of the models numOfChanges < 0.05 : DBCM numOfChanges >= 0.05 | numOfCommittors >= 3 | averageCommitSize < 0.35 : BCCM | averageCommitSize >= 0.35 : DBCM numOfCommittors < 3 : BCCM
- 38. DBCM & BCCM: A Combined Approach Step 2: Applying the models using the selection algorithm
- 39. DBCM & BCCM: A Combined Approach Step 2: Applying the models using the selection algorithm +5%
- 40. Summarizing
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- 49. Summarizing ?