2017Bern Open Repository and Information System (University of Bern)Open access

The False False Positives of Static Analysis

Yuriy Tymchuk

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Abstract

Static analysis tools may produce false positive results, which negatively impact the overall usability of these tools. However, even a correct static analysis report is sometimes classified as a false positive if a developer does not understand it or does not agree with it. Lately developers' classification of false positives is treated on a par with the actual static analysis performance which may distort the knowledge about the real state of static analysis. In this paper we discuss various use cases where a false positive report is not false and the issue is caused by another aspects of static analysis. We provide an in-depth explanation of the issue for each use case followed by recommendations on how to solve it, and thus exemplify the importance of careful false positive classification.

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What this paper is about

Static analysis tools may produce false positive results, which negatively impact the overall usability of these tools. However, even a correct static analysis report is sometimes classified as a false positive if a developer does not understand it or does not agree with it. Lately developers' classification of false positives is treated on a par with the actual static analysis performance which may distort the knowledge about the real state of static analysis. In this paper we discuss various use cases where a false positive report is not false and the issue is caused by another aspects of static analysis. We provide an in-depth explanation of the issue for each use case followed by recommendations on how to solve it, and thus exemplify the importance of careful false positive classification.

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Available abstract

Static analysis tools may produce false positive results, which negatively impact the overall usability of these tools. However, even a correct static analysis report is sometimes classified as a false positive if a developer does not understand it or does not agree with it. Lately developers' classification of false positives is treated on a par with the actual static analysis performance which may distort the knowledge about the real state of static analysis. In this paper we discuss various use cases where a false positive report is not false and the issue is caused by another aspects of static analysis. We provide an in-depth explanation of the issue for each use case followed by recommendations on how to solve it, and thus exemplify the importance of careful false positive classification.

Key concepts: False positive paradox, Static analysis, False positives and false negatives, Computer science, Usability, True positive rate, False accusation, Data mining

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