Software Bug Localization With Markov Logic
ICSE '14: 36th International Conference on Software Engineering Hyderabad India May, 2014(2014)
摘要
Software bug localization is the problem of determining buggy statements in a software system. It is a crucial and expensive step in the software debugging process. Interest in it has grown rapidly in recent years, and many approaches have been proposed. However, existing approaches tend to use isolated information to address the problem, and are often ad hoc. In particular, most existing approaches predict the likelihood of a statement being buggy sequentially and separately.This paper proposes a well-founded, integrated solution to the software bug localization problem based on Markov logic. Markov logic combines first-order logic and probabilistic graphical models by attaching weights to first-order formulas, and views them as templates for features of Markov networks. We show how a number of salient program features can be seamlessly combined in Markov logic, and how the resulting joint inference can be solved.We implemented our approach in a debugging system, called MLNDEBUGGER, and evaluated it on 4 small programs. Our initial results demonstrated that our approach achieved higher accuracy than a previous approach.
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关键词
Automated debugging,Machine learning
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