The DOI matches its Crossref record.
Which warnings should I fix first?
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
On the "naturalness" of buggy code
Ray, B., Hellendoorn, V., Godhane, S., Tu, Z., Bacchelli, A., & Devanbu, P. (2016) · Proceedings of the 38th International Conference on Software Engineering · doi:10.1145/2884781.2884848 · Cited by 252
Predicting Defective Lines Using a Model-Agnostic Technique
Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H., & Matsumoto, K. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3023177 · Cited by 137
Metadata
- Authors
- Kim, S., & Ernst, M. D.
- Venue
- Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
- Publisher
- ACM
- Published
- 2007-09-07
- Pages
- 45-54
- OpenAlex
- W2149598089
- Cited by
- 199
- References
- 0
- FWCI
- 9.21
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Which warnings should I fix first? https://app.cochat.ai/papers/10.1145/1287624.1287633
- The paper
- Kim, S., & Ernst, M. D. (2007). Which warnings should I fix first? Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering. https://doi.org/10.1145/1287624.1287633
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
