@ARTICLE{An17-SQJ-CrashInducingCommits,
  author = {Le An and Foutse Khomh and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc},
  title = {An Empirical Study of Crash-inducing Commits in {M}ozilla {F}irefox},
  journal = {Software Quality Journal ({SQJ})},
  year = {2017},
  month = {March},
  MISSINGvolume = {XXX},
  MISSINGnumber = {XXX},
  pages = {1�-32},
  note = {33 pages.},
  abstract = {Software crashes are dreaded by both software organisations and end-users. Many software organisations have automatic crash reporting tools embedded in their software systems to help quality-assurance teams track and fix crash-related bugs. Previous approaches, which focused on the triaging of crash-types and crash-related bugs, can help software organisations increase their debugging efficiency of crashes. However, these approaches can only be applied after the software systems have been crashing for a certain period of time. To help software organisations detect and fix crash-prone code earlier, we examine the characteristics of commits that lead to crashes, which we call crash-inducing commits, in Mozilla Firefox. We observe that crash-inducing commits are often submitted by developers with less experience and that developers perform more addition and deletion of lines of code in crash-inducing commits but also that they need less effort to fix the bugs caused by these commits. We also characterise commits that would lead to frequent crashes, which impact a large user base, which we call highly impactful crash-inducing commits. Compared to other crash-related bugs, we observe that bugs due to highly impactful crash-inducing commits were less reopened by developers and tend to be fixed by a single commit. We build predictive models to help software organisations detect and fix crash-prone bugs early, when their developers commit code. Our predictive models achieve a precision of 61.2\% and a recall of 94.5\% to predict crash-inducing commits and a precision of 60.9\% and a recall of 91.1\% to predict highly impactful crash-inducing commits. Software organisations could use our models and approach to track and fix crash-prone commits early, before they negatively impact users, thus increasing bug fixing efficiency and user-perceived quality.},
  MISSINGeditor = {XXX},
  grant = {NSERC DG and CRC on Multi-language Systems},
  keywords = {Code and design smells ; SQJ},
  kind = {RIAS},
  language = {english},
  publisher = {Springer},
  url = {http://www.ptidej.net/publications/documents/SQJ17a.doc.pdf}
}

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Many software organisations have automatic crash reporting tools embedded in their software systems to help quality-assurance teams track and fix crash-related bugs. Previous approaches, which focused on the triaging of crash-types and crash-related bugs, can help software organisations increase their debugging efficiency of crashes. However, these approaches can only be applied after the software systems have been crashing for a certain period of time. To help software organisations detect and fix crash-prone code earlier, we examine the characteristics of commits that lead to crashes, which we call crash-inducing commits, in Mozilla Firefox. We observe that crash-inducing commits are often submitted by developers with less experience and that developers perform more addition and deletion of lines of code in crash-inducing commits but also that they need less effort to fix the bugs caused by these commits. We also characterise commits that would lead to frequent crashes, which impact a large user base, which we call highly impactful crash-inducing commits. Compared to other crash-related bugs, we observe that bugs due to highly impactful crash-inducing commits were less reopened by developers and tend to be fixed by a single commit. We build predictive models to help software organisations detect and fix crash-prone bugs early, when their developers commit code. Our predictive models achieve a precision of 61.2% and a recall of 94.5% to predict crash-inducing commits and a precision of 60.9% and a recall of 91.1% to predict highly impactful crash-inducing commits. Software organisations could use our models and approach to track and fix crash-prone commits early, before they negatively impact users, thus increasing bug fixing efficiency and user-perceived quality.","missingeditor":"XXX","grant":"NSERC DG and CRC on Multi-language Systems","keywords":"Code and design smells ; SQJ","kind":"RIAS","language":"english","publisher":"Springer","url":"http://www.ptidej.net/publications/documents/SQJ17a.doc.pdf","bibtex":"@ARTICLE{An17-SQJ-CrashInducingCommits,\n author = {Le An and Foutse Khomh and Yann-Ga{\\\"e}l Gu{\\'e}h{\\'e}neuc},\n title = {An Empirical Study of Crash-inducing Commits in {M}ozilla {F}irefox},\n journal = {Software Quality Journal ({SQJ})},\n year = {2017},\n month = {March},\n MISSINGvolume = {XXX},\n MISSINGnumber = {XXX},\n pages = {1�-32},\n note = {33 pages.},\n abstract = {Software crashes are dreaded by both software organisations and end-users. Many software organisations have automatic crash reporting tools embedded in their software systems to help quality-assurance teams track and fix crash-related bugs. Previous approaches, which focused on the triaging of crash-types and crash-related bugs, can help software organisations increase their debugging efficiency of crashes. However, these approaches can only be applied after the software systems have been crashing for a certain period of time. To help software organisations detect and fix crash-prone code earlier, we examine the characteristics of commits that lead to crashes, which we call crash-inducing commits, in Mozilla Firefox. We observe that crash-inducing commits are often submitted by developers with less experience and that developers perform more addition and deletion of lines of code in crash-inducing commits but also that they need less effort to fix the bugs caused by these commits. We also characterise commits that would lead to frequent crashes, which impact a large user base, which we call highly impactful crash-inducing commits. Compared to other crash-related bugs, we observe that bugs due to highly impactful crash-inducing commits were less reopened by developers and tend to be fixed by a single commit. We build predictive models to help software organisations detect and fix crash-prone bugs early, when their developers commit code. Our predictive models achieve a precision of 61.2\\% and a recall of 94.5\\% to predict crash-inducing commits and a precision of 60.9\\% and a recall of 91.1\\% to predict highly impactful crash-inducing commits. 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