Multilayer feedforward networks are universal approximators
Neural Networks
A vector space model for automatic indexing
Communications of the ACM
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
Using benchmarking to advance research: a challenge to software engineering
Proceedings of the 25th International Conference on Software Engineering
Hipikat: recommending pertinent software development artifacts
Proceedings of the 25th International Conference on Software Engineering
Identifying Reasons for Software Changes Using Historic Databases
ICSM '00 Proceedings of the International Conference on Software Maintenance (ICSM'00)
Populating a Release History Database from Version Control and Bug Tracking Systems
ICSM '03 Proceedings of the International Conference on Software Maintenance
Empirical Software Engineering
OOPSLA '04 Companion to the 19th annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Correlation exploitation in error ranking
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
A Comparison of Bug Finding Tools for Java
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Automatic Mining of Source Code Repositories to Improve Bug Finding Techniques
IEEE Transactions on Software Engineering
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Tracking defect warnings across versions
Proceedings of the 2006 international workshop on Mining software repositories
Prioritizing Software Inspection Results using Static Profiling
SCAM '06 Proceedings of the Sixth IEEE International Workshop on Source Code Analysis and Manipulation
Source Code Analysis: A Road Map
FOSE '07 2007 Future of Software Engineering
Prioritizing Warning Categories by Analyzing Software History
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Which warnings should I fix first?
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Predicting accurate and actionable static analysis warnings: an experimental approach
Proceedings of the 30th international conference on Software engineering
Secure programming with static analysis
Secure programming with static analysis
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Using Static Analysis to Find Bugs
IEEE Software
Z-ranking: using statistical analysis to counter the impact of static analysis approximations
SAS'03 Proceedings of the 10th international conference on Static analysis
Information and Software Technology
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Inferring project-specific bug patterns for detecting sibling bugs
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Hi-index | 0.01 |
In order to improve ineffective warning prioritization of static analysis tools, various approaches have been proposed to compute a ranking score for each warning. In these approaches, an effective training set is vital in exploring which factors impact the ranking score and how. While manual approaches to build a training set can achieve high effectiveness but suffer from low efficiency (i.e., high cost), existing automatic approaches suffer from low effectiveness. In this paper, we propose an automatic approach for constructing an effective training set. In our approach, we select three categories of impact factors as input attributes of the training set, and propose a new heuristic for identifying actionable warnings to automatically label the training set. Our empirical evaluations show that the precision of the top 22 warnings for Lucene, 20 for ANT, and 6 for Spring can achieve 100% with the help of our constructed training set.