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Lecture Notes in Computer Science
A novel framework for predicting regression test failures is proposed. The basic principle embodied in the framework is to use performance analysis tools to capture the runtime behaviour of a program as it executes each test in a regression suite. The performance information is then used to build a dynamically predictive model of test outcomes. Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with state-of-the-art machine learning classifiers. Wedoi:10.1007/978-3-642-39742-4_13 fatcat:7sxkldajdreh7a7d7ektbr2u6e