25th International Conference on Software Engineering, 2003. Proceedings. 2003
DOI: 10.1109/icse.2003.1201224
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Automated support for classifying software failure reports

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Cited by 264 publications
(201 citation statements)
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“…OTC is related to work on test clustering/filtering/indexing [20,21,22,23,24,25]. Previous work performed clustering based on execution profiles, obtained from monitoring test execution.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…OTC is related to work on test clustering/filtering/indexing [20,21,22,23,24,25]. Previous work performed clustering based on execution profiles, obtained from monitoring test execution.…”
Section: Related Workmentioning
confidence: 99%
“…Bounded-exhaustive testing can produce a large number of failing tests, and a tester/developer has to map these failures to distinct faults to submit bug reports or debug the code under test. Our technique builds on the ideas from test clustering [20,21,22,23,24,25] where the goal is to split (failing) tests into groups such that all tests in the same group are likely due to the same underlying fault. Previous work mostly considered manually written tests or actual programs runs, and clustering was based on execution profiles obtained from monitoring test execution.…”
Section: Oracle-based Test Clustering (Otc)mentioning
confidence: 99%
“…Podgurski et al [22] proposed a semi-automated procedure to classify similar faults and plot them by using a Hierarchical Multi Dimension Scaling (HMDS) algorithm. A tool named Xslice [23] visually differentiates the execution slices of passing and failing part of a test.…”
Section: Related Workmentioning
confidence: 99%
“…Francis et al [14] proposed two new tree-based techniques to classify failing executions so that the failing executions resulting from the same cause are grouped together. Podgurski et al [42] proposed to use supervised and unsupervised pattern classification and multivariate visualization to classify failing executions with the related cause. Bowring et al [3] proposed to apply an active learning technique to classify software behavior based on execution data.…”
Section: Application Of Machine Learning In Software Qualitymentioning
confidence: 99%
“…Engineering It is a relatively new topic to apply machine learning to software quality engineering [4,14,18,23,42]. Brun and Ernst [4] isolated fault-revealing properties of a program by applying machine learning to a faulty program and its fixed version.…”
Section: Application Of Machine Learning In Software Qualitymentioning
confidence: 99%