Automated support for classifying software failure reports

By Andy Podgurski, David Leon, Patrick Francis, Wes Masri, Melinda Minch, Jiayang Sun. 25th International Conference on Software Engineering, 2003. Proceedings., 2003

398 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/e092d3ce83826cce36ec9d17b3e4307eac2638e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jiawei Han
2. Xifeng Yan
3. Tao Xie
4. David Lo
5. Michael I. Jordan
6. A. Aiken
7. Alice X. Zheng
8. Ben Liblit

In Information Systems

1. Jiawei Han
2. Xifeng Yan
3. Tao Xie
4. David Lo
5. Chao Liu
6. Rui Abreu
7. Charles Sutton
8. Shan Lu

In Software

1. Rui Abreu
2. Tao Xie
3. Suman Sekhar Jana
4. Yihao Li
5. Ruizhi Gao
6. W. E. Wong
7. Lin Tan
8. Shan Lu

Citations in the corpus

• Statistical Debugging: A Hypothesis Testing-Based Approach (2006, background): “Their subsequent work [ 40 ] first performs feature selection using logistic regression and then clusters failure reports within the space of selected features.”
• Scalable statistical bug isolation (2005, methodology): “[13] apply statistical feature selection, clustering, and multivariate visualization techniques to the task of classifying software failure reports.”
• Have things changed now?: an empirical study of bug characteristics in modern open source software (2006, background): “Many efforts have been made to study fault related characteristics of large software systems [8, 10, 16, 19, 28, 29, 30, 32 , 36].”
• Towards more accurate retrieval of duplicate bug reports (2011, background): “[15], Cubranic and Murphy [16], Pordguski et al [17], and Francis et al.”
• What distinguishes great software engineers? (2019, background): “Bug triaging, examined by many researchers (Anvik and Murphy 2007; Jeong et al. 2009; Podgurski et al. 2003; Runeson et al. 2007; Bertram et al. 2010) is effectively a decision-making process.”
• A Survey on Software Fault Localization (2016, background): “In response, studies have been conducted using programs with multiple bugs [87], [90], [101], [102], [125], [172], [173], [184], [224], [293], [331], [332], [359], [395], [423].”
• Who should fix this bug? (2006, methodology): “Podgurski et al. also applied a machine learning algo-rithm to bug reports, but in their case the algorithm was applied to cluster function call profiles from automated fault reports [10].”
• An approach to detecting duplicate bug reports using natural language and execution information (2008, methodology): “We use the granularity of methods because many previous approaches [6][15][17] did so.”
• SOBER: statistical model-based bug localization (2005, neutral): “Their subsequent work [18] first performs feature selection using logistic regression and in consequence clusters failure reports within the space of selected features.”
• Automatic bug triage using text categorization (2004, methodology): “use machine learning to cluster software failure reports to automatically determine which ones are likely to be manifestations of the same error [9].”
• Using Frankencerts for Automated Adversarial Testing of Certificate Validation in SSL/TLS Implementations (2014, background): “One of their recommendations is to accept recently expired certificates.”
• 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.”