Skip to main content
SpringerLink
Log in

Evolutionary Computation-Based Techniques Over Multiple Data Sets: An Empirical Assessment

  • Research Article-Special Issue-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

A Correction to this article was published on 08 September 2018

This article has been updated

Abstract

In the realm of software testing various organizations wish to predict the faults in their software systems prior to their deployment. This improves the delivered quality and also reduces the maintenance effort. A multitude of software metrics and statistical models have been developed to solve this problem and one such method is called defect prediction. Defect prediction is the process of identifying the defects in the software program prior to its deployment. In recent times, a class of learners called evolutionary computation (EC) techniques has emerged. These EC techniques apply the Darwinian principle of ‘survival of the fittest’. This study performs an empirical assessment of the performance of various EC techniques in the prediction of software defects over multiple data sets. An empirical assessment compares and assesses the performance capability of 16 EC techniques for evaluating the relationship between object-oriented metrics and defect prediction. The developed models are validated using 7 data sets obtained from open source software systems developed by the Software Foundation. On investigating their predictive capabilities and comparative performance, it was found that a majority of EC techniques proved to be highly effective. DTG (a hybridized algorithm) was observed to be the best performing technique. The work done in the current study shows that EC techniques are very effective and can be highly beneficial to testers in the realm of defect prediction in the future.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Change history

  • 08 September 2018

    Unfortunately, Acknowledgment section was missing in the original article. It is given below.

  • 08 September 2018

    Unfortunately, Acknowledgment section was missing in the original article. It is given below.

  • 08 September 2018

    Unfortunately, Acknowledgment section was missing in the original article. It is given below.

  • 08 September 2018

    Unfortunately, Acknowledgment section was missing in the original article. It is given below.

  • 08 September 2018

    Unfortunately, Acknowledgment section was missing in the original article. It is given below.

  • 08 September 2018

    Unfortunately, Acknowledgment section was missing in the original article. It is given below.

  • 08 September 2018

    Unfortunately, Acknowledgment section was missing in the original article. It is given below.

References

  1. Grosan, C.; Abraham, A.: Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds.) Hybrid Evolutionary Algorithms, pp. 1–17. Springer, Berlin Heidelberg (2007)

  2. Elish, K.O.; Elish, M.O.: Predicting defect-prone software modules using support vector machines. J. Syst. Softw. 81(5), 649–660 (2008)

    Article  Google Scholar 

  3. Harman, M.: Why the virtual nature of software makes it ideal for search based optimization. In: International Conference on Fundamental Approaches to Software Engineering, pp. 1–12. Springer, Berlin, Heidelberg (2010)

  4. Jiang, Y.; Cukic, B.; Menzies, T.; Lin, J.: Incremental development of fault prediction models. Int. J. Softw. Eng. Knowl. Eng. 23(10), 1399–1425 (2013)

    Article  Google Scholar 

  5. Chhillar, R.S.: Empirical analysis of object-oriented design metrics for predicting high, medium and low severity faults using mallows C p. ACM SIGSOFT Softw. Eng. Notes 36(6), 1–9 (2011)

    Article  Google Scholar 

  6. Chidamber, S.R.; Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20(6), 476–493 (1994)

    Article  Google Scholar 

  7. Gyimothy, T.; Ferenc, R.; Siket, I.: Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Trans. Softw. Eng. 31(10), 897–910 (2005)

    Article  Google Scholar 

  8. Zhou, Y.; Xu, B.; Leung, H.: On the ability of complexity metrics to predict fault-prone classes in object-oriented systems. J. Syst. Softw. 83(4), 660–674 (2010)

    Article  Google Scholar 

  9. Rodriguez, D.; Ruiz, R.; Riquelme, J.C.; Harrison, R.: A study of subgroup discovery approaches for defect prediction. Inf. Softw. Technol. 55(10), 1810–1822 (2013)

    Article  Google Scholar 

  10. Yu, L.: An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining. Inf. Sci. 191, 31–46 (2012)

  11. Rodrguez, D.; Ruiz, R.; Riquelme, J.C.; Aguilar-Ruiz, J.S.: Searching for rules to detect defective modules: a subgroup discovery approach. Inf. Sci. 191, 14–30 (2012)

    Article  Google Scholar 

  12. Liu, Y.; Khoshgoftaar, T.M.; Seliya, N.: Evolutionary optimization of software quality modeling with multiple repositories. IEEE Trans. Softw. Eng. 36(6), 852–864 (2010)

    Article  Google Scholar 

  13. Catal, C.; Diri, B.: Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem. Inf. Sci. 179(8), 1040–1058 (2009)

    Article  Google Scholar 

  14. De Carvalho, A.B.; Pozo, A.; Vergilio, S.; Lenz, A.: Predicting fault proneness of classes trough a multiobjective particle swarm optimization algorithm. In: ICTAI’08. 20th IEEE International Conference on Tools with Artificial Intelligence, 2008, vol. 2, pp. 387–394. IEEE (2008)

  15. Catal, C.; Diri, B.; Ozumut, B.: An artificial immune system approach for fault prediction in object-oriented software. In: 2nd International Conference on Dependability of Computer Systems, 2007. DepCoS-RELCOMEX’07, pp. 238–245. IEEE (2007)

  16. Lessmann, S.; Baesens, B.; Mues, C.; Pietsch, S.: Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans. Softw. Eng. 34(4), 485–496 (2008)

    Article  Google Scholar 

  17. Shatnawi, R.; Li, W.: The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process. J. Syst. Softw. 81(11), 1868–1882 (2008)

    Article  Google Scholar 

  18. Semwal, V.B.; Mondal, K.; Nandi, G.C.: Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput. Appl. 28, 1–10 (2015)

  19. Semwal, V.B.; Singha, J.; Sharma, P.K.; Chauhan, A.; Behera, B.: An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed. Tools Appl. 76, 1–192 (2016)

  20. Vandecruys, O.; Martens, D.; Baesens, B.; Mues, C.; De Backer, M.; Haesen, R.: Mining software repositories for comprehensible software fault prediction models. J. Syst. Softw. 81(5), 823–839 (2008)

    Article  Google Scholar 

  21. Gondra, I.: Applying machine learning to software fault-proneness prediction. J. Syst. Softw. 81(2), 186–195 (2008)

    Article  Google Scholar 

  22. Kanmani, S.; Uthariaraj, V.R.; Sankaranarayanan, V.; Thambidurai, P.: Object-oriented software fault prediction using neural networks. Inf. Softw. Technol. 49(5), 483–492 (2007)

    Article  Google Scholar 

  23. Di Martino, S.; Ferrucci, F.; Gravino, C.; Sarro, F.: A genetic algorithm to configure support vector machines for predicting fault-prone components. In: International Conference on Product Focused Software Process Improvement, pp. 247–261. Springer, Berlin, Heidelberg (2011)

  24. Azar, D.; Vybihal, J.: An ant colony optimization algorithm to improve software quality prediction models: case of class stability. Inf. Softw. Technol. 53(4), 388–393 (2011)

    Article  Google Scholar 

  25. Pal, A.; Jain, H.; Kumar, M.: Optimizing software error proneness prediction using bird mating algorithm. In: Mahmood, Z. (ed.) Software Project Management for Distributed Computing, pp. 257–287. Springer International Publishing (2017)

  26. Rathore, S.S.; Kumar, S.: Towards an ensemble based system for predicting the number of software faults. Expert Syst. Appl. 82, 357–382 (2017)

    Article  Google Scholar 

  27. Bansiya, J.; Davis, C.G.: A hierarchical model for object-oriented design quality assessment. IEEE Trans. Softw. Eng. 28(1), 4–17 (2002)

    Article  Google Scholar 

  28. Henderson-Sellers, B.: Object-Oriented Metrics: Measures of Complexity. Prentice-Hall Inc, Englewood Cliffs (1995)

    Google Scholar 

  29. Zou, H.; Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, National Institute of Techology Patna, Patna, Bihar, India

    Manju Khari & Prabhat Kumar

Authors
  1. Manju Khari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prabhat Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manju Khari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khari, M., Kumar, P. Evolutionary Computation-Based Techniques Over Multiple Data Sets: An Empirical Assessment. Arab J Sci Eng 43, 3875–3885 (2018). https://doi.org/10.1007/s13369-017-2653-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2653-5

Keywords

Search

Navigation