Computer Science > Logic in Computer Science
arXiv:2501.09225 (cs)
[Submitted on 16 Jan 2025]
Title:Provenance Guided Rollback Suggestions
View a PDF of the paper titled Provenance Guided Rollback Suggestions, by David Zhao and 3 other authors
View PDFHTML (experimental)Abstract:Advances in incremental Datalog evaluation strategies have made Datalog popular among use cases with constantly evolving inputs such as static analysis in continuous integration and deployment pipelines. As a result, new logic programming debugging techniques are needed to support these emerging use cases.
This paper introduces an incremental debugging technique for Datalog, which determines the failing changes for a \emph{rollback} in an incremental setup. Our debugging technique leverages a novel incremental provenance method. We have implemented our technique using an incremental version of the Soufflé Datalog engine and evaluated its effectiveness on the DaCapo Java program benchmarks analyzed by the Doop static analysis library. Compared to state-of-the-art techniques, we can localize faults and suggest rollbacks with an overall speedup of over 26.9$\times$ while providing higher quality results.
| Subjects: | Logic in Computer Science (cs.LO); Programming Languages (cs.PL) |
| Cite as: | arXiv:2501.09225 [cs.LO] |
| (orarXiv:2501.09225v1 [cs.LO] for this version) | |
| https://doi.org/10.48550/arXiv.2501.09225 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Provenance Guided Rollback Suggestions, by David Zhao and 3 other authors
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