ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Yuan:ieeeSE,

• author = "Yuan Yuan and Wolfgang Banzhaf",
• journal = "IEEE Transactions on Software Engineering",
• title = "{ARJA}: Automated Repair of Java Programs via Multi-Objective Genetic Programming",
• year = "2020",
• volume = "46",
• number = "10",
• pages = "1040--1067",
• month = oct,
• keywords = "genetic algorithms, genetic programming, genetic improvement, Program repair, APR, patch generation, multi-objective optimization",
• ISSN = "0098-5589",
• DOI = "doi:10.1109/TSE.2018.2874648",
• size = "28 pages",
• abstract = "Automated program repair is the problem of automatically fixing bugs in programs in order to significantly reduce the debugging costs and improve the software quality. To address this problem, test-suite based repair techniques regard a given test suite as an oracle and modify the input buggy program to make the entire test suite pass. GenProg is well recognized as a prominent repair approach of this kind, which uses genetic programming (GP) to rearrange the statements already extant in the buggy program. However, recent empirical studies show that the performance of GenProg is not fully satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively. Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs. To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code. Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of existing statements. We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches. Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches. In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J. Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.",
• notes = "Also known as \cite{8485732}",

}

Genetic Programming entries for Yuan Yuan Wolfgang Banzhaf

Citations