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Enhanced Evolutionary Automated Program Repair by Finer-Granularity Ingredients and Better Search Algorithms

Authors:

Bo Wang,

Dalin ZhangAuthors Info & Claims

Internetware '22: Proceedings of the 13th Asia-Pacific Symposium on Internetware

Pages 107 - 116

https://doi.org/10.1145/3545258.3545286

Published: 15 September 2022 Publication History

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Abstract

Bug repair is time-consuming and tedious, which hampers software maintenance. To alleviate the burden, automated program repair (APR) is proposed and has been fruitful in the last decade. Evolutionary repair is the seminal work of this field and proliferated a family of approaches. The performance of evolutionary repair approaches is affected by two main factors: (1) search space, which defines all possible patches, and (2) search algorithms, which navigates the space. Although recent approaches have achieved remarkable progress, the main challenges of the two factors still remain. On one hand, the different kinds of search space are very coarse for containing correct patches. On the other hand, the search process guided by genetic algorithms is inefficient to find the correct patches in an appropriate time budget.

In this paper, we propose MicroRepair, a new evolutionary repair approach to address the two challenges. Rather than finding statement-level patches like existing genetic repair approaches, MicroRepair enlarges the search space by breaking the statements into finer-granularity ingredients that are consist of AST leaves. As the search space grows exponentially, the former search algorithms may become inefficient to navigate in the larger space. We utilize the best multi-objective search algorithm selected from our empirical comparison on a set of search algorithms.

We evaluated MicroRepair on 224 bugs of real-world from the benchmark Defects4J and compare it with several state-of-the-art repair approaches. The evaluation results show that MicroRepair correctly repaired 26 bugs with a precision of 62%, which significantly outperforms the state-of-the-art evolutionary APR approaches in terms of precision.

References

[1]

Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2006. An evaluation of similarity coefficients for software fault localization. In 2006 12th Pacific Rim International Symposium on Dependable Computing (PRDC’06). IEEE, 39–46.

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