This paper is about understanding the nature of bug fixing by analyzing thousands of bug fix transactions of software repositories. It then places this learned knowledge in the context of automated program repair. We give extensive empirical results on the nature of human bug fixes at a large scale and a fine granularity with abstract syntax tree differencing. We set up mathematical reasoning on the search space of automated repair and the time to navigate through it. By applying our method on 14 repositories of Java software and 89,993 versioning transactions, we show that not all probabilistic repair models are equivalent.
@cite_29 manually identified 27 bug fix patterns on Java software. Those patterns are precise enough to be automatically extractable from software repositories. They provide and discuss the frequencies of the occurrence of those patterns in 7 open source projects. This work is closely related to ours: we both identify automatically extractable repair actions of software. The main difference is that our repair actions are discovered fully automatically based on AST differencing (there is no prior manual analysis to find them). Furthermore, since our repair actions are meant to be used in an automated program repair setup, they are smaller and more atomic.
@cite_29
2149321161
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.