Provenance Guided Rollback Suggestions

TL;DR

This paper presents a new incremental debugging technique for Datalog that uses provenance information to efficiently identify rollback points, improving fault localization and reducing analysis time in evolving systems.

Contribution

It introduces a novel provenance-based incremental debugging method for Datalog, implemented in Soufflé, that outperforms existing techniques in speed and accuracy.

Findings

Over 26.9× speedup compared to state-of-the-art methods.

Higher quality fault localization and rollback suggestions.

Effective on Java benchmarks analyzed with Doop.

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 Souffl\'{e} 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.