How to find actionable static analysis warnings: A case study with FindBugs

• Main Authors: YEDIDA, Rahul, KANG, Hong Jin, TU, Huy, YANG, Xueqi, LO, David, MENZIES, Tim
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Codes; Computer bugs; false alarms; Industries; locality; hyperparameter optimization; Measurement; software analytics; static analysis; Source coding; Static analysis; Training; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7768; https://ink.library.smu.edu.sg/context/sis_research/article/8771/viewcontent/ActionableStaticAnalysisWarn_av.pdf
• Institution: Singapore Management University

Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should ot be ignored, we suggest that analysts need to look deeper into their algorithms to find choices that better improve the particulars of their specific problem. Specifically, we show here that effective predictors of such warnings can be created by methods that ocally adjust the decision boundary (between actionable warnings and others). These methods yield a new high water-mark for recognizing actionable static code warnings. For eight open-source Java projects (cassandra, jmeter, commons, lucene-solr, maven, ant, tomcat, derby) we achieve perfect test results on 4/8 datasets and, overall, a median AUC (area under the true negatives, true positives curve) of 92%.