1. The influence of developer quality on software fault- proneness prediction Yansong Wu, Yibiao Yang, Yangyang Zhao,Hongmin Lu, Yuming Zhou,Baowen Xu 資訊所 黃大洋 資訊所 楊凱迪 2015/1/13

2. Outline Introduction Developer-quality-based file quality metrics The research question The experimental method & result Conclusion

3. Introduction Reseachers mainly use two types of metrics to build fault-proneness prediction models: Code metrics Process metrics : effective and powerful than code metrics (incorporating developer experience) As a major factor affecting software quality, developer quality has not been applied into fault- proneness prediction models. There are two reason for this: 1.It is difficult to quantify developer quality in a reasonable way. 2.Even with a reasonable measurement of developer quality, we still need to connect developer quality to files in an effective way. This paper quantify the quality of a developer via the percentage of history bug-introduce commits over all his/her commits during the development process.

4. Developer-quality-based file quality metrics

5. Files quality metrics

6. Example of computing the metrics

7. The research question RQ1 (Relationship with existing process metrics) RQ2 (Association with file fault-proneness) RQ3 (Ability to improve fault-proneness prediction)

8. The experimental method & result(RQ1) We use principal component analysis (PCA) to investigate the relationship between our metrics and existing process metrics. This part will show the results of examining their redundancy with traditional process metrics.

9. The experimental method & result(RQ2) we use univariate logistic regression to analyze the association between our metrics and fault-proneness. This part will report the results of investigating their relationship with fault- proneness.

10. The experimental method & result(RQ3) We investigate RQ3 in the typical ranking application scenarios. In the ranking scenario, CE (cost-effectiveness) is the most commonly used performance indictor for evaluating the effort-aware ranking effectiveness of a fault-proneness prediction model. This part will give the results of evaluating their effect for improving the performance of fault-proneness prediction models.

11. Conclusion The first finding shows that these new metrics capture additional information of software quality compared with existing process metrics. The second finding is that most of the new metrics exhibit a significantly association with fault-proneness in expected directions. The third finding reveals that our new metrics can substantially improve the effectiveness of fault-proneness prediction when used with traditional process metrics together. These results suggest that developer quality has a strong influence on software quality and should be taken into account when predicting software fault- proneness.