Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability
<|reference_start|>Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability: Context: Static analyses are well-established to aid in understanding bugs or vulnerabilities during the development process or in large-scale studies. A low false-positive rate is essential for the adaption in practice and for precise results of empirical studies. Unfortunately, static analyses tend to report where a vulnerability manifests rather than the fix location. This can cause presumed false positives or imprecise results. Method: To address this problem, we designed an adaption of an existing static analysis algorithm that can distinguish between a manifestation and fix location, and reports error chains. An error chain represents at least two interconnected errors that occur successively, thus building the connection between the fix and manifestation location. We used our tool CogniCryptSUBS for a case study on 471 GitHub repositories, a performance benchmark to compare different analysis configurations, and conducted an expert interview. Result: We found that 50 % of the projects with a report had at least one error chain. Our runtime benchmark demonstrated that our improvement caused only a minimal runtime overhead of less than 4 %. The results of our expert interview indicate that with our adapted version participants require fewer executions of the analysis. Conclusion: Our results indicate that error chains occur frequently in real-world projects, and ignoring them can lead to imprecise evaluation results. The runtime benchmark indicates that our tool is a feasible and efficient solution for detecting error chains in real-world projects. Further, our results gave a hint that the usability of static analyses may benefit from supporting error chains.<|reference_end|>
@article{wickert2024supporting,
    title={Supporting Error Chains in Static Analysis for Precise Evaluation
  Results and Enhanced Usability},
    author={Anna-Katharina Wickert and Michael Schlichtig and Marvin Vogel and
  Lukas Winter and Mira Mezini and Eric Bodden},
    journal={arXiv preprint arXiv:2403.07808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07808},
    primaryClass={cs.SE}
}