Practical Program Repair via Preference-based Ensemble Strategy

TL;DR

This paper introduces P-EPR, a novel non-learning-based ensemble framework that efficiently ranks APR tools using repair patterns, significantly improving bug-fixing effectiveness and flexibility.

Contribution

It presents the first non-learning-based ensemble method for APR that leverages repair patterns and dynamic updates to enhance bug repair performance.

Findings

P-EPR outperforms existing ensemble strategies in effectiveness.

It demonstrates high flexibility in repairing diverse bugs.

The framework efficiently balances bug fixing and validation costs.

Abstract

To date, over 40 Automated Program Repair (APR) tools have been designed with varying bug-fixing strategies, which have been demonstrated to have complementary performance in terms of being effective for different bug classes. Intuitively, it should be feasible to improve the overall bug-fixing performance of APR via assembling existing tools. Unfortunately, simply invoking all available APR tools for a given bug can result in unacceptable costs on APR execution as well as on patch validation (via expensive testing). Therefore, while assembling existing tools is appealing, it requires an efficient strategy to reconcile the need to fix more bugs and the requirements for practicality. In light of this problem, we propose a Preference-based Ensemble Program Repair framework (P-EPR), which seeks to effectively rank APR tools for repairing different bugs. P-EPR is the first non-learning-based APR ensemble method that is novel in its exploitation of repair patterns as a major source of knowledge for ranking APR tools and its reliance on a dynamic update strategy that enables it to immediately exploit and benefit from newly derived repair results. Experimental results show that P-EPR outperforms existing strategies significantly both in flexibility and effectiveness.