Extraction and Analysis of Knowledge for Automatic Software Repair

Matias Martinez 1
1 SPIRALS - Self-adaptation for distributed services and large software systems
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : Bug fixing is a frequent activity in the software life cycle. The activity aims at removing the gap between the expected behavior of a program and what it actually does. This gap encompasses different anomalies such as the failure of a program facing to a given scenario. Bug fixing is a task historically done by software developers. However, in the recent years, several automatic software repair approaches have emerged to automatically synthesize bug fixes. Unfortunately, bug fixing could be even hard and expensive for automatic program repair approaches. In this thesis, we aim at adding repair approaches strategies to optimize the search of solutions in the repair search space. These strategies consume information extracted from repairs done by developers. Then, for validating the repair approaches and our strategies, we focus on the evaluation of automatic repair approaches. We aim at introducing methodologies for defining how researchers can evaluate repair approaches in a meaningful manner. For example, the performance of a repair approach depends on the defect dataset used to evaluate the approach. First, we define a methodology to define defect datasets that minimize the possibility of biased results.We present a dataset that includes a particular kind of defect: if conditional defects. Then, we aim at measuring the repairability of this kind of defect by evaluating three state-of-the-art automatic software repair approaches.
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Submitted on : Thursday, October 30, 2014 - 4:12:07 PM
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  • HAL Id : tel-01078911, version 1

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Matias Martinez. Extraction and Analysis of Knowledge for Automatic Software Repair. Software Engineering [cs.SE]. Université Lille 1, 2014. English. ⟨tel-01078911⟩

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