Abstract

Automatic bug-finding tools have a high false positive rate: most warnings do not indicate real bugs. Usually bug-finding tools assign important warnings high priority. However, the prioritization of tools tends to be ineffective. We observed the warnings output by three bug-finding tools, FindBugs, JLint, and PMD, for three subject programs, Columba, Lucene, and Scarab. Only 6%, 9%, and 9% of warnings are removed by bug fix changes during 1 to 4 years of the software development. About 90% of warnings remain in the program or are removed during non-fix changes -- likely false positive warnings. The tools' warning prioritization is little help in focusing on important warnings: the maximum possible precision by selecting high-priority warning instances is only 3%, 12%, and 8% respectively.In this paper, we propose a history-based warning prioritization algorithm by mining warning fix experience that is recorded in the software change history. The underlying intuition is that if warnings from a category are eliminated by fix-changes, the warnings are important. Our prioritization algorithm improves warning precision to 17%, 25%, and 67% respectively.

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