A Classification Scheme for Studies on Fault-Prone Components

  • Per Runeson
  • Magnus C. Ohlsson
  • Claes Wohlin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2188)


Various approaches are presented in the literature to identify faultprone components. The approaches represent a wide range of characteristics and capabilities, but they are not comparable, since different aspects are compared and different data sets are used. In order to enable a consistent and fair comparison, we propose a classification scheme, with two parts, 1) a characterisation scheme which captures information on input, output and model characteristics, and 2) an evaluation scheme which is designed for comparing different models’ capabilities. The schemes and the rationale for the elements of the schemes are presented in the paper. Important capabilities to evaluate when comparing different models are rate of misclassification, classification efficiency and total classification cost. Further, the schemes are applied in an example study to illustrate the use of the schemes. It is expected that applying these schemes would help researchers to compare different approaches and thereby enable building of a more consistent knowledge base in software engineering. In addition it is expected to help practitioners to choose a suitable prediction approach for a specific environment by filling out the characterisation scheme and making an evaluation in their own environment.


Evaluation Scheme External Input Cost Ratio Ranking Model Output Type 
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  1. 1.
    Briand, L.C., Basili, V.R. and Hetmanski, C.J., “Developing Interpretable Models with Optimized Set Reduction for Identifying High-Risk Software Components”, IEEE Transactions on Software Engineering, 19(11), 1993, 1028–1044.CrossRefGoogle Scholar
  2. 2.
    Khoshgoftaar, T.M and Lanning, D.L, “A Neural Network Approach for Early Detection of Program Modules Having High Risk in the Maintenance Phase”, Journal of Systems and Software, 29(1), 1995, 85–91.CrossRefGoogle Scholar
  3. 3.
    Khoshgoftaar, T.M., Allen, E.B., Kalaichelvan, K.S and Goel, N.,“Early Quality Prediction: A Case Study in Telecommunications”, IEEE Software (January 1996), 65–71.Google Scholar
  4. 4.
    Khoshgoftaar, T.M, Allen, E.B., Halstead, R. and Trio, G.P., “Detection of Fault-prone Software Modules During a Spiral Life Cycle”, Proceedings of the International Conference on Software Maintenance, (Monterey, California, USA, November 96) 69–76.Google Scholar
  5. 5.
    Ohlsson, N., Helander, M. and Wohlin, C., “Quality Improvement by Identification of Fault-Prone Modules using Software Design Metrics”, In Proceedings Sixth International Conference on Software Quality, (Ottawa, Ontario, Canada, 1996), 1–13.Google Scholar
  6. 6.
    Ohlsson, N. and Alberg, H., “Predicting Fault-Prone Software Modules in Telephone Switches”, IEEE Transactions on Software Engineering, 22(12), 1996, 886–894.CrossRefGoogle Scholar
  7. 7.
    Ohlsson, N. and Wohlin, C., “Experiences of Fault Data in a Large Software System”, Information Technology Management: An International Journal, 2(4), 1998, 163–171.Google Scholar
  8. 8.
    Schneidewind, N. F., “Software Metrics Model for Integrating Quality Control and Prediction”, in Proceedings Fourth International Software Metrics Symposium, (Albuquerque, NM, USA, November 1997) 402–415.Google Scholar
  9. 9.
    Zhao, Ming, Wohlin, C., Ohlsson, N. and Xie, Min, “A Comparison between Software Design and Code Metrics for the Prediction of Software Fault Content”, Information and Software Technology, 40(14), 1998, 801–809.CrossRefGoogle Scholar
  10. 10.
    Khoshgoftaar, T.M. and Allen, E.B., “The Impact of Cost of Misclassification on Software ] Quality Modeling”, in Proceedings Fourth International Software Metrics Symposium, (Albuquerque, NM, USA, November 1997), 54–62.Google Scholar
  11. 11.
    Fenton, N.E. and Pfleeger, S.L., Software Metrics: A Rigorous and Practical Approach, Thomson Computer Press, 1996.Google Scholar
  12. 12.
    Ohlsson, M.C. and Wohlin, C., “Identification of Green, Yellow and Red Legacy Components”, In Proceeding of International Conference on Software Maintenance, (Bethesda, Washington D.C, USA, November 1998), 6–15.Google Scholar
  13. 13.
    ITU, “Recommendation Z.100: SDL-Specification and Description Language”, 1988.Google Scholar
  14. 14.
    McCabe, T. J., “A Complexity Measure”, IEEE Transactions on Software Engineering, 4(2), 1976, 308–320CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Per Runeson
    • 1
  • Magnus C. Ohlsson
    • 1
  • Claes Wohlin
    • 2
  1. 1.Dept. of Communication SystemsLund UniversityLundSweden
  2. 2.Dept. of Software Engineering & Computer ScienceBlekinge Institute of TechnologyRonnebySweden

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