Iterative software fault prediction with a hybrid approach

Erturk E., SEZER E.

APPLIED SOFT COMPUTING, vol.49, pp.1020-1033, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49
  • Publication Date: 2016
  • Doi Number: 10.1016/j.asoc.2016.08.025
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1020-1033
  • Hacettepe University Affiliated: Yes


In this study, we consider a software fault prediction task that can assist a developer during the lifetime of a project. We aim to improve the performance of software fault prediction task while keeping it as applicable. Initial predictions are constructed by Fuzzy Inference Systems (FISs), whereas subsequent predictions are performed by data-driven methods. In this paper, an Artificial Neural Network and Adaptive Neuro Fuzzy Inference System are employed. We propose an iterative prediction model that begins with a FIS when no data are available for the software project and continues with a data-driven method when adequate data become available. To prove the usability of this iterative prediction approach, software fault prediction experiments are performed using expert knowledge for the initial version and information about previous versions for subsequent versions. The datasets employed in this paper comprise different versions of Ant, jEdit, Camel, Xalan, Log4j and Lucene projects from the PROMISE repository. The metrics of the models are common object-oriented metrics, such as coupling between objects, weighted methods per class and response for a class. The results of the models are evaluated according to the receiver operating characteristics with the area under the curve approach. The results indicate that the iterative software fault prediction is successful and can be transformed into a tool that can automatically locate fault-prone modules due to its well-organized information flow. We also implement the proposed methodology as a plugin for the Eclipse environment. (C) 2016 Elsevier B.v. All rights reserved.