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

Classification of software behaviors for failure detection: a discriminative pattern mining approach Classi cation of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach Singapore Management University David Lo Chinese University of Hong Kong Hong Cheng — davidlo@smu.edu.sg Jiawei Han hcheng@se.cuhk.edu.hk Siau-Cheng Khoo and Chengnian Sun National University of Singapore {khoosc,suncn}@comp.nus.edu.sg University of Illinois at Urbana-Champaign hanj@cs.uiuc.edu ABSTRACT Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the di ƒculty 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 rst 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 classi cation. These features are then used to train a classi er to detect failures. Experiments and case studies on traces of several benchmark software systems and a real-life concurrency bug from MySQL http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

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

Association for Computing Machinery — Jun 28, 2009

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References (31)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2009 by ACM Inc.
ISBN
978-1-60558-495-9
doi
10.1145/1557019.1557083
Publisher site
See Article on Publisher Site

Abstract

Classi cation of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach Singapore Management University David Lo Chinese University of Hong Kong Hong Cheng — davidlo@smu.edu.sg Jiawei Han hcheng@se.cuhk.edu.hk Siau-Cheng Khoo and Chengnian Sun National University of Singapore {khoosc,suncn}@comp.nus.edu.sg University of Illinois at Urbana-Champaign hanj@cs.uiuc.edu ABSTRACT Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the di ƒculty 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 rst 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 classi cation. These features are then used to train a classi er to detect failures. Experiments and case studies on traces of several benchmark software systems and a real-life concurrency bug from MySQL

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