Skip to main content
SpringerLink
Log in

Software defect prediction model based on LASSO–SVM

  • S.I.: Intelligent Computing Methodologies in Machine learning for IoT Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A software defect report is a bug in the software system that developers and users submit to the software defect library during software development and maintenance. Managing a software defect report that is overwhelming is a challenging task. The traditional method is manual identification, which is time-consuming and laborious and delays the repair of important software defects. Based on the above background, the purpose of this paper is to study the software defect prediction (SDP) model based on LASSO–SVM. In this paper, the problem of poor prediction accuracy of most SDP models is proposed. A SDP model combining minimum absolute value compression and selection method and support vector machine algorithm is proposed. Firstly, the feature selection ability of the minimum absolute value compression and selection method is used to reduce the dimension of the original data set, and the data set not related to SDP is removed. Then, the optimal value of SVM is obtained by using the parameter optimization ability of cross-validation algorithm. Finally, the SDP is completed by the nonlinear computing ability of SVM. The accuracy of simulation results is 93.25% and 66.67%, recall rate is 78.04%, and f-metric is 72.72%. The results show that the proposed defect prediction model has higher prediction accuracy than the traditional defect prediction model, and the prediction speed is faster.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Mao F, Li B, Shen B (2016) Cross-project software defect prediction based on instance transfer. J Front Comput Sci Technol 10(1):43–55

    Google Scholar 

  2. Wan H, Wu G, Yu M (2019) Software defect prediction based on cost-sensitive dictionary learning. Int J Softw Eng Knowl Eng 29(9):1219–1243

    Article  Google Scholar 

  3. Lee T, Nam J, Han D (2016) Developer micro interaction metrics for software defect prediction. IEEE Trans Softw Eng 42(11):1015–1035

    Article  Google Scholar 

  4. Zhang Z-W, Jing X-Y, Wang T-J (2016) Label propagation based semi-supervised learning for software defect prediction. Autom Softw Eng 24(1):1–23

    Google Scholar 

  5. Nam J, Fu W, Kim S (2018) Heterogeneous defect prediction. IEEE Trans Softw Eng 44(9):874–896

    Article  Google Scholar 

  6. Yu X, Liu J, Peng W (2017) Improving cross-company defect prediction with data filtering. Int J Softw Eng Knowl Eng 27(10):1427–1438

    Article  Google Scholar 

  7. Tantithamthavorn C (2017) An empirical comparison of model validation techniques for defect prediction models. IEEE Trans Softw Eng 43(1):1–18

    Article  Google Scholar 

  8. Herbold S (2017) Comments on ScottKnottESD in response to “An empirical comparison of model validation techniques for defect prediction models”. IEEE Trans Softw Eng PP(99):1091–1094

    Article  Google Scholar 

  9. Li F, Lu Y (2016) Lasso-type estimation for covariate-adjusted linear model. J Appl Stat 45(1):1–17

    MathSciNet  Google Scholar 

  10. Hong L, Dai F, Liu H (2016) A fused-lasso-based doppler imaging algorithm for spinning targets with occlusion effect. IEEE Sens J 16(9):3099–3108

    Article  Google Scholar 

  11. Radmanesh N, Burnett I, Rao B (2016) A lasso-LS optimization with a frequency variable dictionary in a multizone sound system. IEEE/ACM Trans Audio Speech Lang Process 24(3):583–593

    Article  Google Scholar 

  12. Das R, Walia E (2019) Partition selection with sparse autoencoders for content based image classification. Neural Comput Appl 31:675–690

    Article  Google Scholar 

  13. Guo J, Chen Z, Ban Y-L (2017) Precise enumeration of circulating tumor cells using support vector machine algorithm on a microfluidic sensor. IEEE Trans Emerging Top Comput 5(99):518–525

    Article  Google Scholar 

  14. Peng X (2019) A spheres-based support vector machine for pattern classification. Neural Comput Appl 31:379–396

    Article  Google Scholar 

  15. Manavalan B, Shin TH, Lee G (2018) DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest. Oncotarget 9(2):1944–1956

    Article  Google Scholar 

  16. Lang F, Chavarro D, Liu Y (2016) Can automatic classification help to increase accuracy in data collection? J Data Inf Sci 1(3):42–58

    Google Scholar 

  17. Yong H, Zhang X, Feng B et al (2015) iTrade: a mobile data-driven stock trading system with concept drift adaptation. Int J Data Wareh Min 11(1):66–83

    Article  Google Scholar 

  18. Schneidewind NF (2015) Experience with risk-based software defect prediction models. IEEE Trans Syst Man Cybern B Cybern A Publ IEEE Syst Man Cybern Soc 40(4):997–1008

    Google Scholar 

  19. Khoshgoftaar TM, Gao K, Chen Y (2015) Comparing feature selection techniques for software quality estimation using data-sampling-based boosting algorithms. Int J Reliab Qual Saf Eng 22(03):356-13

    Article  Google Scholar 

  20. Kalai Magal R, Jacob SG (2015) Improved random forest algorithm for software defect prediction through data mining techniques. Int J Comput Appl 117(23):18–22

    Google Scholar 

Download references

Acknowledgements

This work was supported by Natural Science Foundation of Heilongjiang Province (Grant No. LH2019F046), Harbin science and technology innovation talents research project (Grant No. 2016RAQXJ013) and Doctoral research fund of Harbin University (Grant No. HUDF2019101).

Author information

Authors and Affiliations

  1. School of Information Engineering, Harbin University, Harbin, 150086, Heilongjiang, China

    Kechao Wang, Lin Liu, Chengjun Yuan & Zhifei Wang

  2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Lin Liu

Authors
  1. Kechao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengjun Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhifei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kechao Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K., Liu, L., Yuan, C. et al. Software defect prediction model based on LASSO–SVM. Neural Comput & Applic 33, 8249–8259 (2021). https://doi.org/10.1007/s00521-020-04960-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04960-1

Keywords

Search

Navigation