Influential Global and Local Contexts Guided Trace Representation for Fault Localization
ACM Transactions on Software Engineering and Methodology(2023)
摘要
Trace data is critical for fault localization (FL) to analyze suspicious statements potentially responsible for a failure. However, existing trace representation meets its bottleneck mainly in two aspects: (1) the trace information of a statement is restricted to a local context (i.e., a test case) without the consideration of a global context (i.e., all test cases of a test suite); (2) it just uses the ‘occurrence’ for representation without strong FL semantics. Thus, we propose UNITE : an infl U ential co N text-Gu I ded T race r E presentation, representing the trace from both global and local contexts with influential semantics for FL. UNITE embodies and implements two key ideas: (1) UNITE leverages the widely used weighting capability from local and global contexts of information retrieval to reflect how important a statement (a word) is to a test case (a document) in all test cases of a test suite (a collection), where a test case (a document) and all test cases of a test suite (a collection) represent local and global contexts respectively; (2) UNITE further elaborates the trace representation from ‘occurrence’ (weak semantics) to ‘influence’ (strong semantics) by combing program dependencies. The large-scale experiments on 12 FL techniques and 20 programs show that UNITE significantly improves FL effectiveness.
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关键词
fault localization,contexts
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