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.
The goal of automatic program repair is to identify a set of syntactic changes that can turn a program that is incorrect with respect to a given specification into a correct one. Existing program repair techniques typically aim to find any program that meets the given specification. Such “best-effort” strategies can end up generating a program that is quite different from the original one. Novel techniques have been proposed to compute syntactically minimal program fixes, but the smallest syntactic fix to a program can still significantly alter the original program’s behaviour. We propose a new approach to program repair based on program distances, which can quantify changes not only to the program syntax but also to the program semantics. We call this the quantitative program repair problem where the “optimal” repair is derived using multiple distances. We implement a solution to the quantitative repair problem in a prototype tool called Qlose (Quantitatively close), using the program synthesizer Sketch. We evaluate the effectiveness of different distances in obtaining desirable repairs by evaluating Qlose on programs taken from educational tools such as CodeHunt and edX.