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Iterative identification of fault-prone binaries using in-process metrics

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Iterative identification of fault-prone binaries using in-process metrics
Code churn, the amount of code change taking place within a software unit over time, has been correlated with fault-proneness in software systems. We investigate the use of code churn and static metrics collected at regular time intervals during the development cycle to predict faults in an iterative, in-process manner. We collected 159 churn and structure metrics from six, four-month snapshots of a 1 million LOC Microsoft product. The number of software faults fixed during each period is recorded per binary module. Using stepwise logistic regression, we create a prediction model to identify fault-prone binaries using three parameters: code churn (the number of new and changed blocks); class Fan In and class Fan Out (normalized by lines of code). The iteratively-built model is 80.0% accurate at predicting faultprone and non-fault-prone binaries. These fault-prediction models have the advantage of allowing the engineers to observe how their fault-prediction profile evolves over time. C...
Lucas Layman, Gunnar Kudrjavets, Nachiappan Nagapp
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where ESEM
Authors Lucas Layman, Gunnar Kudrjavets, Nachiappan Nagappan
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