document: 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 capturing failures and anomalies . On average , our pattern-based classification technique outperforms the baseline approach by 24.68 % in accuracy .
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