Provenance Guided Rollback Suggestions

2025-01-16

AI Summary

Debugging incremental Datalog static analysis in CI/CD pipelines—specifically, rapidly identifying input changes causing analysis failures and recommending optimal rollback points—remains challenging. Method: We propose the first provenance-based debugging approach for incremental Datalog, constructing a fine-grained incremental execution provenance graph, extending the Soufflé engine to support efficient provenance tracking, and integrating it into the Doop static analysis framework. Results: Our method accelerates fault localization and rollback recommendation by 26.9× over state-of-the-art techniques, while significantly improving attribution accuracy and rollback quality. Key contributions are: (1) the first systematic integration of incremental provenance into Datalog debugging; (2) end-to-end automated fault attribution and verifiable rollback decisions; and (3) lightweight, reliable, and scalable debugging support for industrial-grade static analysis pipelines.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

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$ imes$ while providing higher quality results.

Problem

• Datalog
• Static Analysis
• Debugging in Logic Programming

Innovation

• Incremental Debugging
• Datalog Engine
• Error Localization