We present Nopol, an approach for automatically repairing buggy if conditions and missing preconditions. As input, it takes a program and a test suite which contains passing test cases modeling the expected behavior of the program and at least one failing test case embodying the bug to be repaired. It consists of collecting data from multiple instrumented test suite executions, transforming this data into a Satisfiability Modulo Theory (SMT) problem, and translating the SMT result -- if there exists one -- into a source code patch. Nopol repairs object oriented code and allows the patches to contain nullness checks as well as specific method calls.
@cite_3 propose Par, a repair approach using fix patterns representing common ways of fixing common bugs. These fix patterns can avoid the nonsensical patches due to the randomness of some mutation operators. Based on the fix patterns, 119 bugs are examined for patch generation. In this work, the evaluation of patches are contributed by 253 human subjects, including 89 students and 164 developers.
@cite_3
2076719273 2149321161 2784445699 2883590803
Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by , has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations. To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (Par), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically. We experimentally evaluated Par on 119 real bugs. In addition, a user study involving 89 students and 164 developers confirmed that patches generated by our approach are more acceptable than those generated by GenProg. Par successfully generated patches for 27 out of 119 bugs, while GenProg was successful for only 16 bugs. Twenty-seven automatically extractable bug fix patterns are defined using the syntax components and context of the source code involved in bug fix changes. Bug fix patterns are extracted from the configuration management repositories of seven open source projects, all written in Java (Eclipse, Columba, JEdit, Scarab, ArgoUML, Lucene, and MegaMek). Defined bug fix patterns cover 45.7 to 63.3 of the total bug fix hunk pairs in these projects. The frequency of occurrence of each bug fix pattern is computed across all projects. The most common individual patterns are MC-DAP (method call with different actual parameter values) at 14.9---25.5 , IF-CC (change in if conditional) at 5.6---18.6 , and AS-CE (change of assignment expression) at 6.0---14.2 . A correlation analysis on the extracted pattern instances on the seven projects shows that six have very similar bug fix pattern frequencies. Analysis of if conditional bug fix sub-patterns shows a trend towards increasing conditional complexity in if conditional fixes. Analysis of five developers in the Eclipse projects shows overall consistency with project-level bug fix pattern frequencies, as well as distinct variations among developers in their rates of producing various bug patterns. Overall, data in the paper suggest that developers have difficulty with specific code situations at surprisingly consistent rates. There appear to be broad mechanisms causing the injection of bugs that are largely independent of the type of software being produced. Well-designed and publicly available datasets of bugs are an invaluable asset to advance research fields such as fault localization and program repair as they allow directly and fairly comparison between competing techniques and also the replication of experiments. These datasets need to be deeply understood by researchers: the answer for questions like "which bugs can my technique handle?" and "for which bugs is my technique effective?" depends on the comprehension of properties related to bugs and their patches. However, such properties are usually not included in the datasets, and there is still no widely adopted methodology for characterizing bugs and patches. In this work, we deeply study 395 patches of the Defects4J dataset. Quantitative properties (patch size and spreading) were automatically extracted, whereas qualitative ones (repair actions and patterns) were manually extracted using a thematic analysis-based approach. We found that 1) the median size of Defects4J patches is four lines, and almost 30 of the patches contain only addition of lines; 2) 92 of the patches change only one file, and 38 has no spreading at all; 3) the top-3 most applied repair actions are addition of method calls, conditionals, and assignments, occurring in 77 of the patches; and 4) nine repair patterns were found for 95 of the patches, where the most prevalent, appearing in 43 of the patches, is on conditional blocks. These results are useful for researchers to perform advanced analysis on their techniques' results based on Defects4J. Moreover, our set of properties can be used to characterize and compare different bug datasets. The characterization of bug datasets is essential to support the evaluation of automatic program repair tools. In a previous work, we manually studied almost 400 human-written patches (bug fixes) from the Defects4J dataset and annotated them with properties, such as repair patterns. However, manually finding these patterns in different datasets is tedious and time-consuming. To address this activity, we designed and implemented PPD, a detector of repair patterns in patches, which performs source code change analysis at abstract-syntax tree level. In this paper, we report on PPD and its evaluation on Defects4J, where we compare the results from the automated detection with the results from the previous manual analysis. We found that PPD has overall precision of 91 and overall recall of 92 , and we conclude that PPD has the potential to detect as many repair patterns as human manual analysis.