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V. U. B. Challagulla, F. B. Bastani, I.-L. Yen and R. A. Paul, “Empirical Assessment of Machine Learning Based Software Defect Prediction Techniques,” IEEE Int’l Workshop on Object-oriented Real-time Dependable Systems (WORDS), Sedona, 2005, pp. 282-303.

has been cited by the following article:

• TITLE: Temporal Patterns of Software Evolution Defects: A Comparative Analysis of Open Source and Closed Source Projects

  AUTHORS: Uzma Raja, Joanne Elaine Hale, David Peter Hale

  KEYWORDS: Open Source Software, Software Defects, Software Maintenance, Time Series Analysis

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.4 No.8, August 2, 2011

  ABSTRACT: This study examines temporal patterns of software systems defects using the Autoregressive Integrated Moving Average (ARIMA) approach. Defect reports from ten software application projects are analyzed; five of these projects are open source and five are closed source from two software vendors. Across all sampled projects, the ARIMA time series modeling technique provides accurate estimates of reported defects during software maintenance, with organizationally dependent parameterization. In contrast to causal models that require extraction of source-code level metrics, this approach is based on readily available defect report data and is less computation intensive. This approach can be used to improve software maintenance and evolution resource allocation decisions and to identify outlier projects—that is, to provide evidence of unexpected defect reporting patterns that may indicate troubled projects.