Promise 2011: "Empirical validation of human factors on predicting issue resolution time in open source projects" Anh Nguyen Duc, Daniela Cruzes, Claudia Ayala and Reidar Conradi.

1. Empirical validation of human factors in predicting issue lead time in open source projects Nguyen DucAnh, Daniela S. Cruzes, Claudia Ayala and Reidar Conradi 1

2. Outline Introduction Research questions Research methodology Results Conclusions Future work

3. Introduction Software maintenance and evolution Fixing bugs, implementing new feature requests, and enhancing current system features Mozilla bug tracking system receives 170 issue reports/ day, Eclipse projects receives 120 reports/ day (Kim & Whitehead 2006) Issue Lead Time Prediction is challenging due to the: Dynamics of software evolution, and Lack of clear understanding of the factors influencing issue lead time. 3

4. Previous Studies on Issue Lead Time Prediction 4 Main focus is on characteristics of the issue only. Ex: priority, effort, number of comments. Little focus on the Human factors aspect: Developer’s experience, ability, reputation Developer’s collaboration Developer’s capability & collaboration in developing a software module can affect how likely they are to introduce bugs in the module  Are they useful for classifying/ predicting issue lead time as well?

5. Previous Studies on Bug Lead Time Prediction 5

6. Research questions RQ1. Do human factor metrics improve classification of issue lead time? RQ2. Which characteristics of issues increase the predictive power of a linear regression model for predicting issue lead time? RQ3. What is the accuracy of classification/ prediction models achieved? 6

7. Projects 7

8. Issue lead time: Duration between creation time and resolution time Valid issues with stakeholders assignment RESOLVED issues Dependent variable 8

9. Independent variables 9

10. Stakeholder past performance Reporter experience (ExpR) Assignee experience (ExpA) Assignee Average past issue lead time (Apit) Independent variables 10

11. Post submission collaboration The number of comments (NoC) The number of involved stakeholders (NoS) Independent variables 11

12. Research methodology 12

13. Classification results Accuracy of binary classification models 13 Conclusions: Number of commentsand average past issue lead time are effective complementary variables in classifying issue lead time.

14. Univariate and Multivariate analysis Linear regression models Spearman correlation with issue resolution time 14

15. Conclusions RQ1. Do human factor metrics improve classification of issue lead time? Yes. Accuracy improvement up to 12% RQ2. Which human factor metrics contribute significantly to issue lead time prediction in the linear regression models? 15

16. Conclusions RQ3. What are the accuracy of classification/ prediction models can be achieved? 16 Consistent with other studies, but issue report based prediction models yield far from desirable predictive power

17. Future work Investigation of other input variables: mailing list & version control system comments Add more projects to the analysis Use other prediction techniques: non-linear regression Compare open source vs. closed source 17

18. Empirical validation of human factors in predicting issue lead time in open source projects Nguyen DucAnh, Daniela S. Cruzes, Claudia Ayala and Reidar Conradi 18