On The Effectiveness of Dynamic Reduction Techniques in Automated Program Repair

TL;DR

This paper presents a framework that integrates program slicing with automated program repair to efficiently handle large-scale, complex buggy programs, significantly reducing computational costs while maintaining repair quality.

Contribution

It introduces a novel integration of observation-based slicing with APR, improving fault localization, patch generation, and validation processes for large-scale programs.

Findings

Significant reduction in repair time and computational resources.

No degradation in repair quality on the Defects4J dataset.

Enhanced efficiency in large-scale program repair.

Abstract

Repairing a large-scale buggy program using current automated program repair (APR) approaches can be a time-consuming operation that requires significant computational resources. We describe a program repair framework that effectively handles large-scale buggy programs of industrial complexity. The framework exploits program reduction in the form of program slicing to eliminate parts of the code irrelevant to the bug being repaired without adversely affecting the capability of the repair system in producing correct patches. Observation-based slicing is a recently introduced, language-independent slicing technique that shows a good effectiveness in a wide range of applications. In this work, we show how ORBS can be effectively integrated with APR to improve all aspects of the repair process including the fault localization step, patch generation step, and patch validation step. The presented repair framework indeed enhances the capability of APR by reducing the execution cost of a test suite and the search cost for the appropriate faulty statement corresponding to the bug being repair. Our empirical results on the widely used Defects4J dataset reveal that a substantial improvement in performance can be obtained without any degradation in repair quality.