Please use this identifier to cite or link to this item: https://doi.org/10.1145/1557019.1557083
Title: Classification of software behaviors for failure detection: A discriminative pattern mining approach
Authors: Lo, D.
Cheng, H.
Han, J.
Khoo, S.-C. 
Sun, C.
Keywords: Algorithms
Experimentation
Issue Date: 2009
Source: Lo, D.,Cheng, H.,Han, J.,Khoo, S.-C.,Sun, C. (2009). Classification of software behaviors for failure detection: A discriminative pattern mining approach. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 557-565. ScholarBank@NUS Repository. https://doi.org/10.1145/1557019.1557083
Abstract: 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 out-performs the baseline approach by 24.68% in accuracy 1. Copyright 2009 ACM.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/40088
ISBN: 9781605584959
DOI: 10.1145/1557019.1557083
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