TY - GEN
T1 - Automatic construction of an effective training set for prioritizing static analysis warnings
AU - Liang, Guangtai
AU - Wu, Ling
AU - Wu, Qian
AU - Wang, Qianxiang
AU - Xie, Tao
AU - Mei, Hong
PY - 2010
Y1 - 2010
N2 - 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 effec-tive 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.
AB - 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 effec-tive 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.
KW - Generic-bug-related lines
KW - Static analysis tools
KW - Training-set construction
KW - Warning prioritization
UR - http://www.scopus.com/inward/record.url?scp=78649769646&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649769646&partnerID=8YFLogxK
U2 - 10.1145/1858996.1859013
DO - 10.1145/1858996.1859013
M3 - Conference contribution
AN - SCOPUS:78649769646
SN - 9781450301169
T3 - ASE'10 - Proceedings of the IEEE/ACM International Conference on Automated Software Engineering
SP - 93
EP - 102
BT - ASE'10 - Proceedings of the IEEE/ACM International Conference on Automated Software Engineering
T2 - 25th IEEE/ACM International Conference on Automated Software Engineering, ASE'10
Y2 - 20 September 2010 through 24 September 2010
ER -