An Empirical Study of Suppressed Static Analysis Warnings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it