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KDD
2009
ACM

Classification of software behaviors for failure detection: a discriminative pattern mining approach

12 years 5 months ago
Classification of software behaviors for failure detection: a discriminative pattern mining approach
Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the difficulty and complexity of software systems, bugs and anomalies are prevalent. Bugs have caused billions of dollars loss, in addition to privacy and security threats. In this work, we address software reliability issues by proposing a novel method to classify software behaviors based on past history or runs. With the technique, it is possible to generalize past known errors and mistakes to capture failures and anomalies. Our technique first mines a set of discriminative features capturing repetitive series of events from program execution traces. It then performs feature selection to select the best features for classification. These features are then used to train a classifier to detect failures. Experiments and case studies on traces of several benchmark software systems and a real-life concurrency bug from MySQL server show the utility of the technique in c...
David Lo, Hong Cheng, Jiawei Han, Siau-Cheng Khoo,
Added 25 Nov 2009
Updated 25 Nov 2009
Type Conference
Year 2009
Where KDD
Authors David Lo, Hong Cheng, Jiawei Han, Siau-Cheng Khoo, Chengnian Sun
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