Assessing the Applicability of Fault-Proneness Models Across Object-Oriented Software Projects

By Lionel Claude Briand, Walcélio L. Melo, Jürgen Wüst. IEEE Trans. Software Eng., 2002

371 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/d2f043193b4e193d2f8f3b0628e8d9cb11b630fe

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. Rudolf Ferenc
2. Denys Poshyvanyk
3. Andrian Marcus
4. Thomas Zimmermann
5. Erik Arisholm
6. Eivind B. Johannessen
7. N. Nagappan
8. Lionel Claude Briand

In Information Systems

1. Denys Poshyvanyk
2. Rudolf Ferenc
3. Andrian Marcus
4. Thomas Zimmermann
5. Peng He
6. Xiao Liu
7. Bing Li
8. Yutao Ma

In Software

1. Denys Poshyvanyk
2. Andrian Marcus
3. Rudolf Ferenc
4. Thomas Zimmermann
5. Erik Arisholm
6. Eivind B. Johannessen
7. Stefan Lessmann
8. Peng He

Citations in the corpus

• Predicting defects using network analysis on dependency graphs (2008, background): “[10, 11] and identify more predictors and work on assembling the pieces of the puzzle.”
• An empirical study on software defect prediction with a simplified metric set (2014, background): “Catal [28] investigated 90 software defect prediction papers published between 1990 and 2009.”
• Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (2008, background): “However, within a benchmarking context, classifiers should be compared over several data sets from several different software releases and/or projects (see also [9], [44], [52]) and it is extremely unlikely that information on class and cost distributions is available for every data set.”
• Application of neural networks for software quality prediction using object-oriented metrics (2003, background): “One such set of object-oriented metrics is the set proposed by Chidamber and Kemerer (1994). Chidamber and Kemerer also reported empirical data from two commercial organizations and suggested ways in which the metrics could be used to manage OO design efforts. In their paper it was suggested that th”
• Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, methodology): “These methods have been widely used to study the relationships between the metrics and fault or change proneness of classes [4], [6], [9], [13], [39], [63], [72].”
• A systematic and comprehensive investigation of methods to build and evaluate fault prediction models (2010, methodology): “Briand et al. (2002) compared traditional regression techniques with multivariate adaptive regression splines (MARS) (Friedman, 1991).”
• Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes (2007, background): “OO metrics validation studies using fault-proneness as a quality indicator that have appeared since the publication of [7] include [11], [12], [13], [14], [15].”