Influential Global and Local Contexts Guided Trace Representation for Fault Localization

Abstract

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.