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Cited by 36

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Human-In-The-Loop Software Development Agents

Takerngsaksiri, Wannita, Pasuksmit, Jirat, Thongtanunam, Patanamon, Tantithamthavorn, Chakkrit, Zhang, Ruixiong, Jiang, Fan, Li, Jing, Cook, Evan, Chen, Kun, Wu, Ming·2025·2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)
DOI 10.1109/icse-seip66354.2025.00036
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Citations

Cites

Research works printed but not linked: 35

  1. Liu, 2024, Large language model-based agents for software engineering: A survey, arXiv preprint arXiv
  2. 10.1145/3695988 · DOI · Paper page
  3. 10.18653/v1/2023.acl-long.411 · DOI
  4. Yang, 2024, SWE-Agent: Agent-computer interfaces enable automated software engineering, arXiv preprint arXiv
  5. Tao, 2024, Magis: Llm-based multi-agent framework for github issue resolution, arXiv preprint arXiv
  6. Arora, 2024, Masai: Modular architecture for softwareengineering ai agents, arXiv preprint arXiv
  7. Ma, 2024, How to understand whole software repository?, arXiv preprint arXiv
  8. 10.1109/SNPD.2013.92 · DOI
  9. 10.1016/j.infsof.2018.06.001 · DOI
  10. 10.1016/j.infsof.2023.107336 · DOI
  11. 10.1109/COMPSAC61105.2024.00252 · DOI · Paper page
  12. Takerngsaksiri, 2024, PyTester: Deep reinforcement learning for text-to-testcase generation, arXiv preprint arXiv
  13. 10.1016/j.infsof.2024.107565 · DOI
  14. Li, 2022, CodeReviewer: Pre-training for automating code review activities, arXiv preprint arXiv
  15. 10.1016/j.infsof.2024.107523 · DOI
  16. 10.1145/3510003.3510067 · DOI
  17. 10.1145/3540250.3549119 · DOI
  18. 10.1145/3524842.3528452 · DOI · Paper page
  19. Li, 2024, Llm-assisted static analysis for detecting security vulnerabilities, arXiv preprint arXiv
  20. 10.1007/s10664-023-10346-3 · DOI
  21. 10.1145/3540250.3549098 · DOI
  22. 10.1109/ICSE-FoSE59343.2023.00010 · DOI
  23. 10.1109/MS.2023.3268725 · DOI
  24. 10.1109/ICSE.2012.6227210 · DOI · Paper page
  25. 10.1109/ICSME52107.2021.00017 · DOI
  26. 2024, Introducing swe-bench verified
  27. Jimenez, 2023, Swe-bench: Can language models resolve real-world github issues?, arXiv preprint arXiv
  28. Zhang, 2019, Bertscore: Evaluating text generation with bert, arXiv preprint arXiv
  29. Zheng, 2023, Judging llm-as-a-judge with mt-bench and chatbot arena, Advances in Neural Information Processing Systems, 36, 46595
  30. 10.7551/mitpress/9837.003.0013 · DOI
  31. Spencer, 2009, Card sorting: Designing usable categories
  32. 10.11613/BM.2012.031 · DOI · Paper page
  33. Austin, 2021, Program synthesis with large language models, arXiv preprint arXiv
  34. Chen, 2021, Evaluating large language models trained on code, arXiv preprint arXiv
  35. 10.1145/3748647 · DOI

Cited by

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Metadata

Authors
Takerngsaksiri, W., Pasuksmit, J., Thongtanunam, P., Tantithamthavorn, C., Zhang, R., Jiang, F., Li, J., Cook, E., Chen, K., & Wu, M.
Affiliations
  • Monash University,Australia
  • Atlassian,Australia
  • The University of Melbourne,Australia
Venue
2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)
Publisher
IEEE
Published
2025-04-27
Pages
342-352
DOI
10.1109/icse-seip66354.2025.00036
Cited by
36
References
9
Licence

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.