Empirical validation of human factors in predicting issue lead time in open source projects<br />Nguyen DucAnh, Daniela S....
Outline<br />Introduction<br />Research questions<br />Research methodology<br />Results<br />Conclusions<br />Future work...
Introduction<br />Software maintenance and evolution<br />Fixing bugs, implementing new feature requests, and enhancing cu...
Previous Studies on Issue Lead Time Prediction<br />4<br />Main focus is on characteristics of the issue only.<br />Ex: pr...
Previous Studies on Bug Lead Time Prediction<br />5<br />
Research questions<br />RQ1. Do human factor metrics improve classification of issue lead time?<br />RQ2. Which characteri...
Projects<br />7<br />
Issue lead time: <br />Duration between creation time and resolution time<br />Valid issues with stakeholders assignment<b...
Independent variables<br />9<br />
Stakeholder past performance <br />Reporter experience  (ExpR)<br />Assignee experience  (ExpA)<br />Assignee Average past...
Post submission collaboration <br />The number of comments (NoC)<br />The number of involved stakeholders (NoS)<br />Indep...
Research methodology<br />12<br />
Classification results<br />Accuracy of  binary classification models<br />13<br />Conclusions:<br />Number of commentsand...
Univariate and Multivariate  analysis<br />Linear regression models<br />Spearman correlation  with issue resolution time<...
Conclusions<br />RQ1. Do human factor metrics improve classification of issue lead time?<br />Yes.  Accuracy improvement u...
Conclusions<br />RQ3. What are the accuracy of classification/ prediction models can be achieved?<br />16<br />Consistent ...
Future work<br />Investigation of other input variables: mailing list & version control system comments<br />Add more proj...
Empirical validation of human factors in predicting issue lead time in open source projects<br />Nguyen DucAnh, Daniela S....
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Promise 2011: "Empirical validation of human factors on predicting issue resolution time in open source projects"

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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.

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  • I am the SECOND author
  • Can we highlight the new ones here????
  • Assignee Average past issue lead time (APIT)
  • Assignee Average past issue lead time (APIT)Number of Comments (NOC)Number of Stakeholders (NoS)ExpR (Reporter Experience)
  • The regression or correlation coefficients which are positive or negative and significant at 0.01 are marked as “++” or “--”, respectively. The regression or correlation coefficients which are positive/negative and significant at 0.05 are marked as “+” or “-”. Insignificant coefficients are marked as “O”.Description length cannot be used to predict issue lead time. Number of stakeholders and average past issue lead time are good predictors.
  • Not enough for practical use Issue report based precition models yield far from desirable predictive power…Only these onesaremore related to ours… We confirm the results from them also…Most were short papers….
  • Promise 2011: "Empirical validation of human factors on predicting issue resolution time in open source projects"

    1. 1. Empirical validation of human factors in predicting issue lead time in open source projects<br />Nguyen DucAnh, Daniela S. Cruzes,<br />Claudia Ayala and Reidar Conradi<br />1<br />
    2. 2. Outline<br />Introduction<br />Research questions<br />Research methodology<br />Results<br />Conclusions<br />Future work<br />
    3. 3. Introduction<br />Software maintenance and evolution<br />Fixing bugs, implementing new feature requests, and enhancing current system features<br />Mozilla bug tracking system receives 170 issue reports/ day, Eclipse projects receives 120 reports/ day (Kim & Whitehead 2006)<br />Issue Lead Time Prediction is challenging due to the:<br />Dynamics of software evolution, and<br />Lack of clear understanding of the factors influencing issue lead time. <br />3<br />
    4. 4. Previous Studies on Issue Lead Time Prediction<br />4<br />Main focus is on characteristics of the issue only.<br />Ex: priority, effort, number of comments.<br />Little focus on the Human factors aspect:<br />Developer’s experience, ability, reputation<br />Developer’s collaboration<br />Developer’s capability & collaboration in developing a software module can affect how likely they are to introduce bugs in the module<br /> Are they useful for classifying/ predicting issue lead time as well?<br />
    5. 5. Previous Studies on Bug Lead Time Prediction<br />5<br />
    6. 6. Research questions<br />RQ1. Do human factor metrics improve classification of issue lead time?<br />RQ2. Which characteristics of issues increase the predictive power of a linear regression model for predicting issue lead time?<br />RQ3. What is the accuracy of classification/ prediction models achieved?<br />6<br />
    7. 7. Projects<br />7<br />
    8. 8. Issue lead time: <br />Duration between creation time and resolution time<br />Valid issues with stakeholders assignment<br />RESOLVED issues<br />Dependent variable<br />8<br />
    9. 9. Independent variables<br />9<br />
    10. 10. Stakeholder past performance <br />Reporter experience (ExpR)<br />Assignee experience (ExpA)<br />Assignee Average past issue lead time (Apit)<br />Independent variables<br />10<br />
    11. 11. Post submission collaboration <br />The number of comments (NoC)<br />The number of involved stakeholders (NoS)<br />Independent variables<br />11<br />
    12. 12. Research methodology<br />12<br />
    13. 13. Classification results<br />Accuracy of binary classification models<br />13<br />Conclusions:<br />Number of commentsand average past issue lead time are effective complementary variables in classifying issue lead time.<br />
    14. 14. Univariate and Multivariate analysis<br />Linear regression models<br />Spearman correlation with issue resolution time<br />14<br />
    15. 15. Conclusions<br />RQ1. Do human factor metrics improve classification of issue lead time?<br />Yes. Accuracy improvement up to 12%<br />RQ2. Which human factor metrics contribute significantly to issue lead time prediction in the linear regression models?<br />15<br />
    16. 16. Conclusions<br />RQ3. What are the accuracy of classification/ prediction models can be achieved?<br />16<br />Consistent with other studies, but issue report based prediction models yield far from desirable predictive power<br />
    17. 17. Future work<br />Investigation of other input variables: mailing list & version control system comments<br />Add more projects to the analysis<br />Use other prediction techniques: non-linear regression<br />Compare open source vs. closed source<br />17<br />
    18. 18. Empirical validation of human factors in predicting issue lead time in open source projects<br />Nguyen DucAnh, Daniela S. Cruzes,<br />Claudia Ayala and Reidar Conradi<br />18<br />

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