Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Improving fault localization with pre-training

  • Letter
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Wong W E, Gao R, Li Y, Abreu R, Wotawa F. A survey on software fault localization. IEEE Transactions on Software Engineering, 2016, 42(8): 707–740

    Article  Google Scholar 

  2. Feng Z, Gu D, Tang D, Duan N, Feng X, Gong M, Shou L, Qin B, Liu T, Jiang D, Zhou M. CodeBERT: a pre-trained model for programming and natural languages. In: Proceedings of Findings of the Association for Computational Linguistics. 2020, 1536–1547

  3. Sohn J, Yoo S. FLUCCS: using code and change metrics to improve fault localization. In: Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. 2017, 273–283

  4. Zhang Z, Lei Y, Mao X, Yan M, Xu L, Zhang X. A study of effectiveness of deep learning in locating real faults. Information and Software Technology, 2021, 131: 106486

    Article  Google Scholar 

  5. Pearson S, Campos J, Just R, Fraser G, Abreu R, Ernst M D, Pang D, Keller B. Evaluating and improving fault localization. In: Proceedings of the 39th IEEE/ACM International Conference on Software Engineering. 2017, 609–620

  6. Li X, Li W, Zhang Y, Zhang L. DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization. In: Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. 2019, 169–180

  7. Yan Y, Cheng D, Feng J E, Li H, Yue J. Survey on applications of algebraic state space theory of logical systems to finite state machines. Science China Information Sciences, 2023, 66(1): 111201

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, 511363, China

    Zhuo Zhang

  2. Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University, Ningbo, 315000, China

    Ya Li

  3. Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, 210209, China

    Jianxin Xue

  4. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Xiaoguang Mao

Authors
  1. Zhuo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ya Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianxin Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoguang Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ya Li.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Li, Y., Xue, J. et al. Improving fault localization with pre-training. Front. Comput. Sci. 18, 181205 (2024). https://doi.org/10.1007/s11704-023-2597-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-023-2597-8

Search

Navigation