A Systematic Data Collection Procedure for Software Defect Prediction

Goran Mauša1, Tihana Galinac Grbac1 and Bojana Dalbelo Bašić2

  1. Faculty of Engineering
    Vukovarska 58, 51 000 Rijeka, Croatia
    gmausa@riteh.hr, tgalinac@riteh.hr
  2. Faculty of Electrical Engineering and Computing
    Unska 3, 10 000 Zagreb, Croatia


Software defect prediction research relies on data that must be collected from otherwise separate repositories. To achieve greater generalization of the results, standardized protocols for data collection and validation are necessary. This paper presents an exhaustive survey of techniques and approaches used in the data collection process. It identifies some of the issues that must be addressed to minimize dataset bias and also provides a number of measures that can help researchers to compare their data collection approaches and evaluate their data quality. Moreover, we present a data collection procedure that uses a bug-code linking technique based on regular expression. The detailed comparison and root cause analysis of inconsistencies with a number of popular data collection approaches and their publicly available datasets, reveals that our procedure achieves the most favorable results. Finally, we implement our data collection procedure in a data collection tool we name the Bug-Code (BuCo) Analyzer.

Key words

software defect prediction, data collection issues, dataset bias, bug-code linking, open-source projects

Digital Object Identifier (DOI)


Publication information

Volume 13, Issue 1 (January 2016)
Year of Publication: 2016
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Mauša, G., Grbac, T. G., Bašić, B. D.: A Systematic Data Collection Procedure for Software Defect Prediction. Computer Science and Information Systems, Vol. 13, No. 1, 173–197. (2016), https://doi.org/10.2298/CSIS141228061M