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The advance in machine learning (ML)-driven natural language process (NLP) points a promising direction for automatic bug fixing for software programs, as fixing a buggy program can be transformed to a translation task. While software programs contain much richer information than one-dimensional natural language documents, pioneering work on using ML-driven NLP techniques for automatic program repair only considered a limited set of such information. We hypothesize that more comprehensive information of software programs, if appropriately utilized, can improve the effectiveness of ML-driven NLP approaches in repairing software programs. As the first step towards proving this hypothesis, we propose a unified representation to capture the syntax, data flow, and control flow aspects of software programs, and devise a method to use such a representation to guide the transformer model from NLP in better understanding and fixing buggy programs. Our preliminary experiment confirms that the more comprehensive information of software programs used, the better ML-driven NLP techniques can perform in fixing bugs in these programs.
Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to fix software bugs automatically. While promising, these approaches have two major limitations. Their se
We introduce Learn2fix, the first human-in-the-loop, semi-automatic repair technique when no bug oracle--except for the user who is reporting the bug--is available. Our approach negotiates with the user the condition under which the bug is observed.
Relative correctness is the property of a program to be more-correct than another with respect to a given specification. Whereas the traditional definition of (absolute) correctness divides candidate program into two classes (correct, and incorrect),
Despite significant advances in automatic program repair (APR)techniques over the past decade, practical deployment remains an elusive goal. One of the important challenges in this regard is the general inability of current APR techniques to produce
Automatic program repair (APR) has seen a growing interest in recent years with numerous techniques proposed. One notable line of research work in APR is search-based techniques which generate repair candidates via syntactic analyses and search for v