An extensive comparison of bug prediction approaches

By Marco D'Ambros, Michele Lanza, Romain Robbes. IEEE Working Conference on Mining Software Repositories, 2010

640 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/7feb6a3936b175373fe1f9b20718caa43542a400

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. Ahmed E. Hassan
2. Chakkrit Kla Tantithamthavorn
3. Ken-ichi Matsumoto
4. Shane McIntosh
5. Jaechang Nam
6. Sunghun Kim
7. Vincent J. Hellendoorn
8. Sinno Jialin Pan

In Information Systems

1. Ahmed E. Hassan
2. Chakkrit Kla Tantithamthavorn
3. Ken-ichi Matsumoto
4. Shane McIntosh
5. Premkumar T. Devanbu
6. Vincent J. Hellendoorn
7. Romain Robbes
8. Alberto Bacchelli

In Software

1. Ahmed E. Hassan
2. Chakkrit Kla Tantithamthavorn
3. Ken-ichi Matsumoto
4. Shane McIntosh
5. Vincent J. Hellendoorn
6. Premkumar T. Devanbu
7. Jaechang Nam
8. Sunghun Kim

Citations in the corpus

• Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016, methodology): “[6, 7], and 3 are provided by Zimmermann et al.”
• On the "Naturalness" of Buggy Code (2015, methodology): “DP is typically used at release-time to predict post-release bugs [35, 57, 42, 36, 11]; so, for this comparison we use the post release bug data collected in Phase-II.”
• Transfer defect learning (2013, methodology): “AEEEM consists of 61 metrics: 17 source code metrics, 5 previous-defect metrics, 5 entropy-of-change metrics, 17 entropy-of-source-code metrics, and 17 churn-of-sourcecode metrics [20].”
• An investigation on the feasibility of cross-project defect prediction (2011, background): “…defect predictors (Menzies et al. 2007b; Ohlsson and Alberg 1996; Nagappan and Ball 2005; Nagappan et al. 2006; Ostrand et al. 2005; Weyuker et al. 2008; Catal and Diri 2009; Hassan and Holt 2005; Wahyudin et al. 2008; Carvalho et al. 2010; D’Ambros et al. 2010; Jureczko and Spinellis 2010).”
• Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, methodology): “1 Mende (2010) attempted to replicate two defect prediction experiments, including an earlier version of this one (D’Ambros et al. 2010).”
• The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, background): “First, defect prediction models can be used to predict modules that are likely to be defect-prone in the future [1], [2], [3], [4], [5], [6], [7], [8], [9].”
• A systematic review of machine learning techniques for software fault prediction (2015, background): “Hence, total number of 122 studies [1, 2, 20-139] were identified for further processing and analysis.”
• An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, result): “Such unrealistic and unstable performance estimates could lead to incorrect model selection in practice and inaccurate conclusions for defect prediction studies [70], [105].”
• The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “[22], [23], as well as High Performance Computing systems that are provided by the Compute Canada(7) and the Centre for Advanced Computing at Queen’s University.”