Data Mining Static Code Attributes to Learn Defect Predictors
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TLDR
It is shown that static code attributes used to build defect predictors are much more important than which particular attributes are used, and contrary to prior pessimism, they are demonstrably useful and yield predictors with a mean probability of detection and mean false alarms rates.Abstract:
The value of using static code attributes to learn defect predictors has been widely debated. Prior work has explored issues like the merits of "McCabes versus Halstead versus lines of code counts" for generating defect predictors. We show here that such debates are irrelevant since how the attributes are used to build predictors is much more important than which particular attributes are used. Also, contrary to prior pessimism, we show that such defect predictors are demonstrably useful and, on the data studied here, yield predictors with a mean probability of detection of 71 percent and mean false alarms rates of 25 percent. These predictors would be useful for prioritizing a resource-bound exploration of code that has yet to be inspectedread more
Citations
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Journal ArticleDOI
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
TL;DR: A framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository, showing an appealing degree of predictive accuracy, which supports the view that metric-based classification is useful.
Journal ArticleDOI
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
TL;DR: Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.
Proceedings ArticleDOI
A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction
TL;DR: A comparative analysis of the predictive power of two different sets of metrics for defect prediction indicates that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.
Proceedings ArticleDOI
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
TL;DR: This paper studied cross-project defect prediction models on a large scale and identified factors that do influence the success of cross- project predictions, and derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted.
Journal ArticleDOI
On the relative value of cross-company and within-company data for defect prediction
TL;DR: It is demonstrated in this paper that the minimum number of data samples required to build effective defect predictors can be quite small and can be collected quickly within a few months.
References
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