Finding failures by cluster analysis of execution profiles

By William Dickinson, David Leon, Andy Podgurski. Proceedings of the 23rd International Conference on Software Engineering. ICSE 2001, 2001

305 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/fecfaf9282d5679765c15625398f8d7f96996c93

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Citations in the corpus

Finding latent code errors via machine learning over program executions (2004, background): “[4] use machine learning over program executions, with the assumption that it is cheap to execute a program but expensive to verify the correctness of each execution.”
Automated support for classifying software failure reports (2003, methodology): “Dickinson, et al present a technique called cluster filtering for filtering test cases [4][5].”
A Survey on Software Fault Localization (2016, unknown): “[90], [101], [102], [125], [172], [173], [184], [224], [293], [331], [332], [359], [395], [423].”
A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization (2013, methodology): “…fault and then conducts regression testing to localize the next fault [Jones et al. 2007], while the parallel debugging approach tries to locate faults simultaneously, using the technique of fault-focusing clustering [Dickinson et al. 2001; Podgurski et al. 2003; Liu and Han 2006; Zheng et al.”
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis (2009, background): “However, manually inspecting system logs to detect anomalies is unfeasible due to the increasing scale and complexity of distributed systems.”
Using Frankencerts for Automated Adversarial Testing of Certificate Validation in SSL/TLS Implementations (2014, background): “As we show in Section IX, several Web browsers show just the “Expired certificate” warning even if the expired certificate is not issued by a trusted CA and the connection is thus insecure.”
Mining Invariants from Console Logs for System Problem Detection (2010, background): “Compared with the state of art, our approach can not only detect numerous real problems with high accuracy but also provide intuitive insight into the problems.”
Log Clustering Based Problem Identification for Online Service Systems (2016, methodology): “For example, Dickenson et al. [1] collected execution traces and used classification techniques to categorize the collected traces based on some string distance metrics.”
Detecting object usage anomalies (2007, background): “[10] to find faulty executions based on their profiles.”
Statistical Debugging: A Hypothesis Testing-Based Approach (2006, background): “find program failures through clustering program execution profiles [39].”
SOBER: statistical model-based bug localization (2005, background): “find program failures through clustering program execution profiles [6].”