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• W2023490832 abstract "Automated debugging techniques aim to help developers locate and understand the cause of a failure, an extremely challenging yet fundamental task. Most state-of-the-art approaches suffer from two problems: they require a large number of passing and failing tests and report possible faulty code with no explanation. To mitigate these issues, we present MIMIC, a novel automated debugging technique that combines and extends our previous input generation and anomaly detection techniques. MIMIC (1) synthesizes multiple passing and failing executions similar to an observed failure and (2) uses these executions to detect anomalies in behavior that may explain the failure. We evaluated MIMIC on six failures of real-world programs with promising results: for five of these failures, MIMIC identified their root causes while producing a limited number of false positives. Most importantly, the anomalies identified by MIMIC provided information that may help developers understand (and ultimately eliminate) such root causes." @default.
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• W2023490832 date "2014-09-15" @default.
• W2023490832 modified "2023-10-18" @default.
• W2023490832 title "MIMIC" @default.
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• W2023490832 doi "https://doi.org/10.1145/2642937.2643014" @default.
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