Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76937
Title: Contract-based program repair without the contracts
Authors: Chen, LS 
Pei, Y 
Furia, CA
Issue Date: 2017
Publisher: IEEE
Source: ASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, 2017, 8115674, p. 637-647 How to cite?
Abstract: Automated program repair (APR) is a promising approach to automatically fixing software bugs. Most APR techniques use tests to drive the repair process; this makes them readily applicable to realistic code bases, but also brings the risk of generating spurious repairs that overfit the available tests. Some techniques addressed the overfitting problem by targeting code using contracts (such as pre- and postconditions), which provide additional information helpful to characterize the states of correct and faulty computations; unfortunately, mainstream programming languages do not normally include contract annotations, which severely limits the applicability of such contract-based techniques. This paper presents JAID, a novel APR technique for Java programs, which is capable of constructing detailed state abstractions - similar to those employed by contract-based techniques - that are derived from regular Java code without any special annotations. Grounding the repair generation and validation processes on rich state abstractions mitigates the overfitting problem, and helps extend APR's applicability: in experiments with the DEFECTS4J benchmark, a prototype implementation of JAID produced genuinely correct repairs, equivalent to those written by programmers, for 25 bugs - improving over the state of the art of comparable Java APR techniques in the number and kinds of correct fixes.
Description: 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 30 Oct.-3 Nov. 2017
URI: http://hdl.handle.net/10397/76937
ISBN: 978-1-5386-2684-9 (electronic)
978-1-5386-3976-4 (Print on Demand(PoD))
DOI: 10.1109/ASE.2017.8115674
Appears in Collections:Conference Paper

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