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A Framework for Software Defect Prediction Using Optimal Hyper-Parameters of Deep Neural Network

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1794))

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Abstract

Software defect prediction (SDP) models are widely used to identify the defect-prone modules in the software system. SDP model can help to reduce the testing cost, resource allocation, and improve the quality of software. We propose a specific framework of optimized deep neural network (ODNN) to develop a SDP system. The best hyper-parameters of ODNN are selected using the stage-wise grid search-based optimization technique. ODNN involves feature scaling, oversampling, and configuring the base DNN model. The performance of the ODNN model on 16 datasets is compared with the standard machine learning algorithms viz. Naïve Bayes, Support Vector Machine, Random Forest, Ada Boost, and base DNN model in terms of Accuracy, F-measure, and Area Under Curve (AUC). Experimental results show that the ODNN framework outperforms base DNN (BDNN) with 11.90% (accuracy), 0.26 (f-measure), and 0.13 (AUC). The statistical analysis using Wilcoxon signed-rank test and Nemenyi test show that the proposed framework is more effective than state-of-the-art models.

Supported by organization IIT (BHU) Varanasi India.

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Notes

  1. 1.

    https://github.com/klainfo/DefectData.

  2. 2.

    https://github.com/klainfo/DefectData.

References

  1. Thota, M.K., Francis, H., Shajin, Rajesh, P., et al.: Survey on software defect prediction techniques. Int. J. Appli. Sci. Eng. 17(4), 331–344 (2020)

    Google Scholar 

  2. Rathore, S.S., Kumar, S.: A study on software fault prediction techniques. Artif. Intell. Rev. 51(2), 255–327 (2019)

    Google Scholar 

  3. Pachouly, J., Ahirrao, S., Kotecha, K., Selvachandran, G., Abraham, A.: A systematic literature review on software defect prediction using artificial intelligence: Datasets, data validation methods, approaches, and tools. Eng. Appl. Artif. Intell. 111, 104773 (2022)

    Article  Google Scholar 

  4. Omri, S., Sinz, C.: Deep learning for software defect prediction: a survey. In: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops, pp. 209–214 (2020)

    Google Scholar 

  5. Kumar, L., Misra, S., Rath, S.K.: An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes. Comput. Standards Interfaces 53,1–32 (2017)

    Google Scholar 

  6. Corinna, C., Vladimir, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/bf00994018

  7. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  8. Murphy, K.P.: Naive bayes classifiers, university of british columbia (2006)

    Google Scholar 

  9. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1), 119–139 (1997)

    Google Scholar 

  10. Matloob, F.: A systematic literature review. IEEE Access, Software defect prediction using ensemble learning (2021)

    Google Scholar 

  11. Samir, M., El-Ramly, M., Kamel, A.: Investigating the use of deep neural networks for software defect prediction. In: 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pp. 1–6 (2019)

    Google Scholar 

  12. Chatterjee, S., Maji, B.: A bayesian belief network based model for predicting software faults in early phase of software development process. Appl. Intell. 48(8), 2214–2228 (2018)

    Article  Google Scholar 

  13. Kumar, R., Chaturvedi, A., Kailasam, L.: An unsupervised software fault prediction approach using threshold derivation. IEEE Trans. Reliab. 71(2), 911–932 (2022)

    Article  Google Scholar 

  14. Pontes, F.J., Amorim, G.F., Balestrassi, P.P., Paiva, A.P., Ferreira, J.R.: Design of experiments and focused grid search for neural network parameter optimization. Neurocomput., 186, 22–34 (2016)

    Google Scholar 

  15. Zimmermann, T., Nagappan, N., Zeller, A.: Predicting bugs from history. In: Softw. Evol., pp. 69–88. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-76440-3_4

    Chapter  Google Scholar 

  16. Malhotra, R., Kamal, S.: An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data. Neurocomputing 343, 120–140 (2019)

    Article  Google Scholar 

  17. Wilcoxon, F.: Individual comparisons by ranking methods. Int. Biometric Society 1(3), 80–83 (1945)

    Google Scholar 

  18. Nemenyi, P.B.: Distribution-free multiple comparisons. Nemenyi 1963, Princeton University (1963)

    Google Scholar 

  19. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Rakesh Kumar & Amrita Chaturvedi

Authors
  1. Rakesh Kumar
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  2. Amrita Chaturvedi
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Corresponding author

Correspondence to Rakesh Kumar .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Kumar, R., Chaturvedi, A. (2023). A Framework for Software Defect Prediction Using Optimal Hyper-Parameters of Deep Neural Network. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_14

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  • DOI: https://doi.org/10.1007/978-981-99-1648-1_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1647-4

  • Online ISBN: 978-981-99-1648-1

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