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ICSE2022顶会

PROPR: Property-Based Automatic Program Repair

Matthías Páll Gissurarson, Leonhard Applis, Annibale Panichella, Arie van Deursen, David Sands

2022年份
13被引次数

摘要

Automatic program repair (APR) regularly faces the challenge of overfitting patches -patches that pass the test suite, but do not actually address the problems when evaluated manually. Currently, overfit detection requires manual inspection or an oracle making quality control of APR an expensive task. With this work, we want to introduce properties in addition to unit tests for APR to address the problem of overfitting. To that end, we design and implement PropR, a program repair tool for Haskell that leverages both property-based testing (via QuickCheck) and the rich type system and synthesis offered by the Haskell compiler. We compare the repair-ratio, time-to-first-patch and overfitting-ratio when using unit tests, property-based tests, and their combination. Our results show that properties lead to quicker results and have a lower overfit ratio than unit tests. The created overfit patches provide valuable insight into the underlying problems of the program to repair (e.g., in terms of fault localization or test quality). We consider this step towards fitter, or at least insightful, patches a critical contribution to bring APR into developer workflows. CCS CONCEPTS • Software and its engineering → Search-based software engineering; Automatic programming; Functional languages; Source code generation.

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