Skip to main content
SpringerLink
Log in

Literature Review: A Comparative Study of Software Defect Prediction Techniques

  • Conference paper
  • First Online:
Proceedings of 3rd International Conference on Artificial Intelligence: Advances and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

  • 266 Accesses

Abstract

At the latest, a lot of studies have been conducted to forecast software flaws and determine if modules are defective or not, in order to build high-quality software at a low cost before executing the testing stage. To solve the limitations of prediction, researchers have used a variety of machine learning techniques to build models. Though the time and resources required utilizing these procedures are calculated to be less because study concentrates on only those modules that are projected to be problematic. But a disproportionately big portion of the studies demonstrated low prediction capability, and their accuracy for varied datasets was also not persistent. In this literature survey paper, all the techniques have been studied and analyzed for predicting the bugs in the software. All the approaches being adopted by the researchers for handling the dimension reduction, noise filtering and class imbalancing have been studied in this review. There is still a need for handling feature selection, noise filtering, and class imbalancing, which can further affect the performance and accuracy of the model. To further comprehend these flaws, a full review has been undertaken from 2016 to 2021 period in order to gain a better knowledge of how the various models proposed by a number of authors are still lacking in terms of performing better on the available datasets and accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dam HK, Pham T, Ng SW, Tran T, Grundy J, Ghose A, Kim T, Kim CJ (2019) Lessons learned from using a deep tree-based model for software defect prediction in practice. In: Proceedings of 16th international conference on mining software repositories (MSR). IEEE, Montreal, QC, Canada, pp 46–57

    Google Scholar 

  2. Jin C (2021) Software defect prediction model based on distance metric learning. Soft Comput:447–461. Springer

    Google Scholar 

  3. Shenvi AA (2021) Defect prevention with orthogonal defect classification. In: Proceedings of the 2nd India software engineering conference. pp 83–88

    Google Scholar 

  4. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res:1205–1224

    Google Scholar 

  5. Gao K, Khoshgoftaar TM, Wang H, Seliya N (2020) Choosing software metrics for defect prediction: an investigation on feature selection techniques. Softw: Pract Exp 41(5):579–606. Wiley Online Library

    Google Scholar 

  6. Rodriguez D, Ruiz R, Cuadrado-Gallego J, Aguilar-Ruiz J, Garre M (2007) Attribute selection in software engineering datasets for detecting fault modules. In: 33rd EUROMICRO Conference on software engineering and advanced applications, EUROMICRO 2007. IEEE, pp 418–423

    Google Scholar 

  7. Peng Y, Wu Z, Jiang J (2010) A novel feature selection approach for biomedical data classification. J Biomed Inform 43(1):15–23. Elsevier

    Google Scholar 

  8. Sharma D, Chandra P (2018) Software fault prediction using machine-learning techniques. In: Smart computing and informatics. Springer, Singapore, pp 541–549

    Google Scholar 

  9. Meiliana SK, Warnars HLHS, Gaol FL, Abdurachman E, Soewito B (2017) Software metrics for fault prediction using machine learning approaches: A literature review with PROMISE repository dataset, In: 2017 IEEE international conference on cybernetics and computational intelligence (CyberneticsCom). IEEE, pp 19–23

    Google Scholar 

  10. Ran L, Zhou L, Zhang S, Liu H, Huang X, Sun Z (2019) Software defect prediction based on ensemble learning. In: Proceedings of the 2019 2nd International conference on data science and information technology. pp 1–6

    Google Scholar 

  11. Matloob F, Aftab S, Iqbal A (2019). A framework for software defect prediction using feature selection and ensemble learning techniques. Int J Mod Educ & Comput Sci 11 (12)

    Google Scholar 

  12. Khan MZ (2020) Hybrid ensemble learning technique for software defect prediction. Int J Mod Educ & Comput Sci 12(1)

    Google Scholar 

  13. Matloob F, Ghazal TM, Taleb N, Aftab S, Ahmad M, Khan MA (2021) Software defect prediction using ensemble learning: a systematic literature review. IEEE Access:98754–98771

    Google Scholar 

  14. Agrawal P, Abutarboush HF, Ganesh T, Mohamed AW (2021) Metaheuristic algorithms on feature selection: a survey of one decade of research (2009-2019). IEEE Access 9:26766-26791

    Google Scholar 

  15. Oluwagbemiga BA, Shuib B, AbdulKadir SJ, Sobri AH (2019) A hybrid multi-filter wrapper feature selection method for software defect predictors

    Google Scholar 

  16. Raheem M, Ameen A, Ayinla F, Ayeyemi B (2020) Software defect prediction using metaheuristic algorithms and classification techniques. Ilorin J Comput Sci Inf Technol 3(1):23-39. Bennin KE, Keung J, Phannachitta P, Monden A, Mensah S (2017) Mahakil: Diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction. IEEE Trans Softw Eng 44(6):534-550

    Google Scholar 

  17. Alweshah M, Alkhalaileh S, Albashish D, Mafarja M, Bsoul Q, Dorgham O (2021) A hybrid mine blast algorithm for feature selection problems. Soft Comput 25(1):517–534

    Article  Google Scholar 

  18. Suresh Kumar P, Behera HS, Nayak J, Naik B (2021) Bootstrap aggregation ensemble learning-based reliable approach for software defect prediction by using characterized code feature. Innov Syst Softw Eng:1–25

    Google Scholar 

  19. Goyal S, Bhatia PK (2020) Heterogeneous stacked ensemble classifier for software defect prediction. In: 2020 Sixth International conference on parallel, distributed and grid computing (PDGC). pp 126–130. IEEE

    Google Scholar 

  20. Alsawalqah H, Hijazi NM, Eshtay M, Faris H, Radaideh AA, Aljarah I, Alshamaila Y (2020) Software defect prediction using heterogeneous ensemble classification based on segmented patterns. Appl.Sci 10(5):1745

    Google Scholar 

  21. Jiang K, Iwahori Y, Wang A, Wu H, Zhang Y (2020) Heterogeneous defect prediction based on transfer learning to handle extreme imbalance. Appl Sci 10(1):396

    Google Scholar 

  22. Chakraborty T, Chakraborty AK (2020) Hellinger net: A hybrid imbalance learning model to improve software defect prediction. IEEE Trans Reliab, IEEE 70(2):481-494

    Google Scholar 

  23. Bejjanki KK, Gyani J, Gugulothu N (2020) Class imbalance reduction (CIR): a novel approach to software defect prediction in the presence of class imbalance. MDPI, Symmetry 12(3):407

    Google Scholar 

  24. Wei H, Hu C, Chen S, Xue Y, Zhang Q (2009) Establishing a software defect prediction model via effective dimension reduction. Inf Sci 477:399-409

    Google Scholar 

  25. Khuat TH, Le MH (2019) Ensemble learning for software fault prediction problem with imbalanced data. Int J Electr Comput Eng 9(4):3241

    Google Scholar 

  26. Cai X, Niu Y, Geng S, Zhang J, Cui Z, Li J, Chen J (2019) An under-sampled software defect prediction method based on hybrid multi-objective cuckoo search. Concurr Comput: Pract Exp 32(5):e5478

    Google Scholar 

  27. NezhadShokouhi MM, Majidi MA, Rasoolzadegan A (2020) Software defect prediction using over-sampling and feature extraction based on Mahalanobis distance. J Supercomput 76(1):602-635

    Google Scholar 

  28. Ghosh S, Rana A, Kansal V (2018) A nonlinear manifold detection-based model for software defect prediction. Procedia Comput Sci 132:581–594. Elsevier

    Google Scholar 

  29. Miholca DL (2018) An improved approach to software defect prediction using a hybrid machine learning model. In: 2018 20th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC), IEEE. pp 443–448

    Google Scholar 

  30. Nawaz A, Rehman AU, Abbas M (2020) A novel multiple ensemble learning models based on different datasets for software defect prediction. arXiv preprint arXiv: 2008.13114. Cornell University

    Google Scholar 

  31. Tomar D, Agarwal S (2016) Prediction of defective software modules using class imbalance learning, Applied Computational Intelligence and Soft Computing Volume 2016. Article ID 7658207:6

    Google Scholar 

  32. Bahaweres RB, Agustian F, Hermadi I, Suroso AI (2020) Software defect prediction using neural network based SMOTE. In: 7th international conference on electrical engineering, computer sciences and informatics (EECSI)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amity School of Engineering and Technology, Gurgaon, Haryana, India

    Tarunim Sharma, Aman Jatain & Shalini Bhaskar

  2. Maharaja Surajmal Institute, Janakpuri, New Delhi, India

    Kavita Pabreja

Authors
  1. Tarunim Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aman Jatain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shalini Bhaskar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kavita Pabreja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tarunim Sharma .

Editor information

Editors and Affiliations

  1. Poornima College of Engineering, Jaipur, Rajasthan, India

    Garima Mathur

  2. Poornima College of Engineering, Jaipur, Rajasthan, India

    Mahesh Bundele

  3. Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India

    Ashish Tripathi

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharma, T., Jatain, A., Bhaskar, S., Pabreja, K. (2023). Literature Review: A Comparative Study of Software Defect Prediction Techniques. In: Mathur, G., Bundele, M., Tripathi, A., Paprzycki, M. (eds) Proceedings of 3rd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7041-2_2

Download citation

Publish with us

Policies and ethics

Search

Navigation