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2008
IEEE-ACM

Predicting accurate and actionable static analysis warnings: an experimental approach

12 years 2 months ago
Predicting accurate and actionable static analysis warnings: an experimental approach
Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening. Categories and Subject Descriptors D.2.4 [Software/Program Verification]: Rel...
Joseph R. Ruthruff, John Penix, J. David Morgentha
Added 17 Nov 2009
Updated 09 Dec 2009
Type Conference
Year 2008
Where ICSE
Authors Joseph R. Ruthruff, John Penix, J. David Morgenthaler, Sebastian G. Elbaum, Gregg Rothermel
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