An Empirical Study of Suppressed Static Analysis Warnings

Scalable static analyzers are popular tools for finding incorrect, inefficient, insecure, and hard-to-maintain code early during the development process. Because not all warnings reported by a static analyzer are immediately useful to developers, many static analyzers provide a way to suppress warnings, e.g., in the form of special comments added into the code. Such suppressions are an important mechanism at the interface between static analyzers and software developers, but little is currently known about them. This paper presents the first in-depth empirical study of suppressions of static analysis warnings, addressing questions about the prevalence of suppressions, their evolution over time, the relationship between suppressions and warnings, and the reasons for using suppressions. We answer these questions by studying projects written in three popular languages and suppressions for warnings by four popular static analyzers. Our findings show that (i) suppressions are relatively common, e.g., with a total of 7,357 suppressions in 46 Python projects, (ii) the number of suppressions in a project tends to continuously increase over time, (iii) surprisingly, 50.8% of all suppressions do not affect any warning and hence are practically useless, (iv) some suppressions, including useless ones, may unintentionally hide future warnings, and (v) common reasons for introducing suppressions include false positives, suboptimal configurations of the static analyzer, and misleading warning messages. These results have actionable implications, e.g., that developers should be made aware of useless suppressions and the potential risk of unintentional suppressing, that static analyzers should provide better warning messages, and that static analyzers should separately categorize warnings from third-party libraries.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-19
DOI
https://doi.org/10.1145/3715729
Primary Topic
Software Engineering Research
Type
article
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article

An Empirical Study of Suppressed Static Analysis Warnings

Michael Pradel, Yingying Wang, Julia Rubin, Huimin Hu
Proceedings of the ACM on software engineering.
Software Engineering Research
article

An Empirical Study of Suppressed Static Analysis Warnings

Michael Pradel, Yingying Wang, Julia Rubin, Huimin Hu
article en

Abstract

Scalable static analyzers are popular tools for finding incorrect, inefficient, insecure, and hard-to-maintain code early during the development process. Because not all warnings reported by a static analyzer are immediately useful to developers, many static analyzers provide a way to suppress warnings, e.g., in the form of special comments added into the code. Such suppressions are an important mechanism at the interface between static analyzers and software developers, but little is currently known about them. This paper presents the first in-depth empirical study of suppressions of static analysis warnings, addressing questions about the prevalence of suppressions, their evolution over time, the relationship between suppressions and warnings, and the reasons for using suppressions. We answer these questions by studying projects written in three popular languages and suppressions for warnings by four popular static analyzers. Our findings show that (i) suppressions are relatively common, e.g., with a total of 7,357 suppressions in 46 Python projects, (ii) the number of suppressions in a project tends to continuously increase over time, (iii) surprisingly, 50.8% of all suppressions do not affect any warning and hence are practically useless, (iv) some suppressions, including useless ones, may unintentionally hide future warnings, and (v) common reasons for introducing suppressions include false positives, suboptimal configurations of the static analyzer, and misleading warning messages. These results have actionable implications, e.g., that developers should be made aware of useless suppressions and the potential risk of unintentional suppressing, that static analyzers should provide better warning messages, and that static analyzers should separately categorize warnings from third-party libraries.

Proceedings of the ACM on software engineering.Vol. 2(FSE)
University of Stuttgart (DE), University of British Columbia (CA)
Peace, Justice and strong institutions
Openalex Percentile: Top 80%
Software Engineering Research
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An Empirical Study of Suppressed Static Analysis Warnings — Michael Pradel, Yingying Wang, et al. · Proceedings of the ACM on software engineering. (2025) | TGRS Research Map | TGRS