Applied multivariate statistical analysis
Applied multivariate statistical analysis
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Bugs as deviant behavior: a general approach to inferring errors in systems code
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
OOPSLA '04 Companion to the 19th annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Correlation exploitation in error ranking
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Mining metrics to predict component failures
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Tracking defect warnings across versions
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Experimental program analysis: a new program analysis paradigm
Proceedings of the 2006 international symposium on Software testing and analysis
Looking for bugs in all the right places
Proceedings of the 2006 international symposium on Software testing and analysis
Evaluating static analysis defect warnings on production software
PASTE '07 Proceedings of the 7th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Prioritizing Warning Categories by Analyzing Software History
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Automating algorithms for the identification of fault-prone files
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Which warnings should I fix first?
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Z-ranking: using statistical analysis to counter the impact of static analysis approximations
SAS'03 Proceedings of the 10th international conference on Static analysis
A report on a survey and study of static analysis users
DEFECTS '08 Proceedings of the 2008 workshop on Defects in large software systems
XFindBugs: eXtended FindBugs for AspectJ
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Accurate Interprocedural Null-Dereference Analysis for Java
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Making defect-finding tools work for you
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Proceedings of the 19th international symposium on Software testing and analysis
Linux kernel developer responses to static analysis bug reports
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Automatic construction of an effective training set for prioritizing static analysis warnings
Proceedings of the IEEE/ACM international conference on Automated software engineering
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Proceedings of the FSE/SDP workshop on Future of software engineering research
Information and Software Technology
Bug prediction based on fine-grained module histories
Proceedings of the 34th International Conference on Software Engineering
Active refinement of clone anomaly reports
Proceedings of the 34th International Conference on Software Engineering
Extending static analysis by mining project-specific rules
Proceedings of the 34th International Conference on Software Engineering
Dynamically validating static memory leak warnings
Proceedings of the 2013 International Symposium on Software Testing and Analysis
A comparative evaluation of static analysis actionable alert identification techniques
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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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.