How Process Enactment Data Affects Product Defectiveness Prediction - A Case Study

Part of the Studies in Computational Intelligence book series (SCI, volume 496)


The quality of a software product is highly influenced by the software process used to develop it. However, abstract and dynamic nature of the software process makes its measurement difficult, and this difficulty has supported the assessment insight of indirectly measuring the performance of software process by using the characteristics of the developed product. In fact, enactment of the software process might have a significant effect on product characteristics and data, and therefore, on the use of measurement and analysis results. In this article, we report a case study that aimed to investigate the effect of process enactment data on product defectiveness in a small software organization. We carried out the study by defining and following a methodology that included the application of Goal-Question-Metric (GQM) approach to direct analysis, the utilization of a questionnaire to assess usability of metrics, and the application of machine learning methods to predict product defectiveness. The results of the case study showed that the accuracy of predictions varied according to the machine learning method used, but in the overall, about 3% accuracy improvement was achieved by including process enactment data in the analysis.


software defect prediction machine learning process enactment software measurement defectiveness 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Koru, A.G., Liu, H.: Building Effective Defect-Prediction Models in Practice. IEEE Software 22(6) (November/December 2005)Google Scholar
  2. 2.
    Lee, T., Nam, J., Han, D., Kim, S., In, H.P.: Micro Interaction Metrics for Defect Prediction. In: ESEC/FSE 2011 Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering (2011)Google Scholar
  3. 3.
    Sivrioğlu, D., Tarhan, A.: Defectiveness Analysis According To Software Module Features: A Case Study (Yazılım Modül Özelliklerine Göre Hatalılık Analizi: Bir Durum Çalışması) Original is Turkish (February 2012)Google Scholar
  4. 4.
    Dhiauddin, M.: Defect Prediction Model For Testing Phase. Master Thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System (May 2009)Google Scholar
  5. 5.
    Zeng, H., Rine, D.: Estimation of Software Defects Fix Effort Using Neural Networks. In: COMPSAC 2004 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts, USA, vol. 02, pp. 20–21 (2004)Google Scholar
  6. 6.
    Weiss, C., Premraj, R., Zimmermann, T., Zeller, A.: How Long will it Take to Fix This Bug? In: MSR 2007 Proceedings of the Fourth International Workshop on Mining Software Repositories, USA, p. 1 (2007)Google Scholar
  7. 7.
    Hassouna, A., Tahvildari, T.: An Effort Prediction Framework for Software Defect Correction. Information and Software Technology 52, 197–209 (2010)CrossRefGoogle Scholar
  8. 8.
    Hewett, R., Kijsanayothin, P.: On Modeling Software Defect Repair Time. Empir. Software Eng. 14, 165–186 (2008, 2009)CrossRefGoogle Scholar
  9. 9.
    Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empirical Software Eng. 14, 131–164 (2009)CrossRefGoogle Scholar
  10. 10.
    Florac, A.W., Park, R.E., Carleton, A.D.: Practical Software Measurement: Measuring for Process Management and Improvement. Guidebook: CMU/SEI-97-HB-003 (1997) Google Scholar
  11. 11.
    Çatal, Ç., Diri, B.: A Systematic Review of Software Fault Prediction Studies. Expert Systems with Applications 36, 7346–7354 (2009)CrossRefGoogle Scholar
  12. 12. (last access date: April 11, 2012)
  13. 13.
    Basili, V.R., Caldiera, G., Rombach, H.D.: Goal Question Metric Paradigm. In: Encyclopedia of Software Engineering – 2 Volume Set (1994) ISBN#1-54004-8Google Scholar
  14. 14. (last access date: April 11, 2012)
  15. 15.
    Jalote, P., Dinesh, K., Raghavan, S., Bhashyam, R., Ramakrishnan, M.: Quantitative Quality Management through Defect Prediction and Statistical Process ControlGoogle Scholar
  16. 16.
    Wahyudin, D., Schatten, A., Winkler, D., Tjoa, A.M., Biffl, S.: Defect Prediction using Combined Product and Project Metrics a Case Study from the Open Source “Apache” MyFaces Project Family. In: 34th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2008, September 3-5, pp. 207–215 (2008)Google Scholar
  17. 17.
    Fenton, N., Krause, M., Neil, P.: A Probabilistic Model for Software Defect Prediction. For submission to IEEE Transactions in Software EngineeringGoogle Scholar
  18. 18.
    Tarhan, A., Demirörs, O.: Apply Quantitative Management Now. IEEE Software 29(3), 77–85 (2012), doi:10.1109/MS.2011.91CrossRefGoogle Scholar
  19. 19.
    Tarhan, A., Demirörs, O.: Investigating the Effect of Variations in Test Development Process: A Case from a Safety-Critical System. Software Quality Journal, doi:10.1007/s11219-011-9129-8Google Scholar
  20. 20.
    Boetticher, G.D.: Nearest Neighbor Sampling for Better Defect PredictionGoogle Scholar
  21. 21.
    Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier (2005)Google Scholar
  22. 22.
    CMMI Product Team, CMMI for Development, Version 1.3, Technical Report, SEI (2010) Google Scholar
  23. 23.
    Sivrioğlu, D.: A Method for Product Defectiveness Prediction with Process Enactment Data in a Small Software Organization. Master Thesis, Middle East Technical University, Informatics Institute (June 2012) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Simsoft Computer Technologies Co., LtdTechnopolis of METUAnkaraTurkey
  2. 2.Department of Software EngineeringHacettepe UniversityAnkaraTurkey
  3. 3.Informatics InstituteMETUAnkaraTurkey

Personalised recommendations