Precise Condition Synthesis for Program Repair

Abstract

Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, a typical automatic repair technique tries to modify the program to make all tests pass. However, since the test suites in real world projects are usually insufficient, aiming at passing the test suites often leads to incorrect patches. This problem is known as weak test suites or overfitting.In this paper we aim to produce precise patches, that is, any patch we produce has a relatively high probability to be correct. More concretely, we focus on condition synthesis, which was shown to be able to repair more than half of the defects in existing approaches. Our key insight is threefold. First, it is important to know what variables in a local context should be used in an "if" condition, and we propose a sorting method based on the dependency relations between variables. Second, we observe that the API document can be used to guide the repair process, and propose document analysis technique to further filter the variables. Third, it is important to know what predicates should be performed on the set of variables, and we propose to mine a set of frequently used predicates in similar contexts from existing projects.Based on the insight, we develop a novel program repair system, ACS, that could generate precise conditions at faulty locations. Furthermore, given the generated conditions are very precise, we can perform a repair operation that is previously deemed to be too overfitting: directly returning the test oracle to repair the defect. Using our approach, we successfully repaired 17 defects on four projects of Defects4J, which is the largest number of fully automatically repaired defects reported on the dataset so far. More importantly, the precision of our approach in the evaluation is 73.9%, which is significantly higher than previous approaches, which are usually less than 40%.

iFixR: bug report driven program repair

“…Since the seminal work by Weimer et al [83] who relied on genetic programming to evolve program variants until one variant is found to satisfy the functional constraints of a test suite, the community has been interested in test-based techniques to repair programs without specifications. Thus, various approaches [12,13,18,19,25,26,32,33,38,45,48,49,52,53,55,62,65,83,84,92,93] have been proposed in the literature aiming at reducing manual debugging efforts through automatically generating patches. Beyond fixing syntactic errors, i.e., cases where the code violates some programming language specifications [16], the current challenges lie in fixing semantic bugs, i.e., cases where implementation of program behavior deviates from developer's intention [22,61].…”

A Survey of Test Based Automatic Program Repair

“…Recently, Xiong et al [24] demonstrate the strength of hints provided by variable dependency and code document when synthesis patches in ACS.…”

Contract-based program repair without the contracts

“…We quantitatively compare JAID to all other available tools for APR of Java programs that have also used DEFECTS4J in their evaluations: 4 1) jGenProg is the implementation of GenProg [14], [33]-which works on C-for Java programs; we refer to jGenProg's evaluation in [19]; 2) jKali is the implementation of Kali [28]-which works on C-for Java programs; we refer to jKali's evaluation in [19]; 3) Nopol focuses on fixing Java conditional expression; we refer to Nopol's evaluation in [19]; 4) xPAR is a reimplementation of PAR [12]-which is not publicly available-discussed in [13] and [35]; 5) HDA implements the "history-driven" technique of [13]; 6) ACS implements the "precise condition synthesis" of [35].…”

“…MintHint [10] also builds a statistical model to generate repair suggestions consisting of expressions that may be useful in a complete fix. ACS [35] is a recent technique that significantly improves the precision of condition synthesis based on a combination of data-and control-dependency analysis, and mining API documentation and Boolean predicates in existing projects. JAID also relies on data-and control-dependency analysis, and can guess modifications to conditional expressions, but it does not need any additional source of information other than the project being fixed.…”

“…These results are close to, or outperform, other comparable tools for the automated program repair of Java programs in terms of total number of correct repairs and precision, and include the first automatically produced correct repairs for 14 bugs of DEFECTS4J that were previously outside the capabilities of APR. JAID is also the first APR technique that achieves high levels of precision without relying on additional input other than tests and faulty code; in contrast, other recent highprecision APR techniques [13], [35] analyze a large number of project repositories to collect additional information that guides fixing. This paper's key contributions, which bolster JAID's performance, include techniques to build a rich abstraction of object state.…”