Recent empirical studies show that the performance of GenProg is not 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 the 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.
@cite_85 developed a repair method based on semantic code search, called SearchRepair. This method encodes a database of human-written code fragments as SMT constraints on the input-output behavior and searches the database for potential fixes with an input-output specification. Experiments on small C programs written by students showed that SearchRepair can generate higher-quality repairs than GenProg, AE and RSRepair.
@cite_85
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Automated program repair can potentially reduce debugging costs and improvesoftware quality but recent studies have drawn attention to shortcomings inthe quality of automatically generated repairs. We propose a new kind ofrepair that uses the large body of existing open-source code to findpotential fixes. The key challenges lie in efficiently finding codesemantically similar (but not identical) to defective code and thenappropriately integrating that code into a buggy program. We presentSearchRepair, a repair technique that addresses these challenges by(1) encoding a large database of human-written code fragments as SMTconstraints on input-output behavior, (2) localizing a given defect to likelybuggy program fragments and deriving the desired input-output behavior forcode to replace those fragments, (3) using state-of-the-art constraintsolvers to search the database for fragments that satisfy that desiredbehavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program testsuites. We find that SearchRepair repairs 150 (19 ) of 778 benchmark Cdefects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by thefour techniques by measuring how many independent, not-used-during-repairtests they pass, and find that SearchRepair-repaired programs pass 97.3 ofthe tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repairedprograms pass 68.7 , 72.1 , and 64.2 of the tests, respectively. We concludethat SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects those tools cannot.