PreprintCited by 33Software Engineering ResearchSoftware Engineering Techniques and PracticesAdvanced Software Engineering Methodologies

Codepori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology

Open access
Rasheed, Zeeshan, Malik, Sami, Kemell, Kai-Kristian, Muhammad, Waseem, Saari, Mika, Systä, Kari, Abrahamsson, Pekka·2024·Research Square

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2139/ssrn.4979510

Cites

267 of 267 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 35

  1. Liu, J., Wang, K., Chen, Y., Peng, X., Chen, Z., Zhang, L., Lou, Y.: Large language model-based agents for software engineering: A survey. arXiv preprint arXiv:2409.02977 (2024) · arXiv
  2. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)
  3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in Neural Information Processing Systems 33, 1877-1901 (2020)
  4. Jin, H., Huang, L., Cai, H., Yan, J., Li, B., Chen, H.: From llms to llm-based agents for software engineering: A survey of current, challenges and future. arXiv preprint arXiv:2408.02479 (2024) · arXiv
  5. Hassan, A.E., Li, H., Lin, D., Adams, B., Chen, T.-H., Kashiwa, Y., Qiu, D.: Agentic software engineering: Foundational pillars and a research roadmap. arXiv preprint arXiv:2509.06216 (2025) · arXiv
  6. Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S.K.S., Lin, Z., Zhou, L., et al.: Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352 (2023) · arXiv
  7. Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., Liu, Z., Sun, M.: Com- municative agents for software development. arXiv preprint arXiv:2307.07924 (2023) · arXiv
  8. Huang, D., Zhang, J.M., Luck, M., Bu, Q., Qing, Y., Cui, H.: Agentcoder: Multi-agent-based code generation with iterative testing and optimisation. arXiv preprint arXiv:2312.13010 (2023) · arXiv
  9. Guo, T., Chen, X., Wang, Y., Chang, R., Pei, S., Chawla, N.V., Wiest, O., Zhang, X.: Large language model based multi-agents: A survey of progress and challenges. arXiv preprint arXiv:2402.01680 (2024) · arXiv
  10. Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., Wang, Z., Wang, Z., Yin, F., Zhao, J., et al.: Exploring large language model based intelligent agents: Definitions, methods, and prospects. arXiv preprint arXiv:2401.03428 (2024) · arXiv
  11. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021) · arXiv
  12. Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al.: Program synthesis with large language models. arXiv preprint arXiv:2108.07732 (2021) · arXiv
  13. Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., et al.: Measuring coding challenge competence with apps. arXiv preprint arXiv:2105.09938 (2021) · arXiv
  14. Jimenez, C.E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., Narasimhan, K.: Swe-bench: Can language models resolve real-world github issues? arXiv preprint arXiv:2310.06770 (2023) · arXiv
  15. Tang, X., Liu, Y., Cai, Z., Shao, Y., Lu, J., Zhang, Y., Deng, Z., Hu, H., An, K., Huang, R., et al.: Ml-bench: Evaluating large language models and agents for machine learning tasks on repository-level code. arXiv preprint arXiv:2311.09835 (2023) · arXiv
  16. Wang, K., Li, T., Zhang, X., Wang, C., Sun, W., Liu, Y., Shi, B.: Software devel- opment life cycle perspective: A survey of benchmarks for code large language models and agents. arXiv preprint arXiv:2505.05283 (2025) · arXiv
  17. Thiergart, J., Huber, S., Übellacker, T.: Understanding emails and drafting responses-an approach using gpt-3. arXiv preprint arXiv:2102.03062 (2021) · arXiv
  18. Rasheed, Z., Waseem, M., Kemell, K.-K., Xiaofeng, W., Duc, A.N., Systä, K., Abrahamsson, P.: Autonomous agents in software development: A vision paper. arXiv preprint arXiv:2311.18440 (2023) · arXiv
  19. Tufano, M., Drain, D., Svyatkovskiy, A., Deng, S.K., Sundaresan, N.: Unit test case generation with transformers and focal context. arXiv preprint arXiv:2009.05617 (2020) · arXiv
  20. Nascimento, N., Alencar, P., Cowan, D.: Comparing software developers with chatgpt: An empirical investigation. arXiv preprint arXiv:2305.11837 (2023) · arXiv
  21. Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., Wang, H.: Large language models for software engineering: A systematic literature review. arXiv preprint arXiv:2308.10620 (2023) · arXiv
  22. Zheng, Z., Ning, K., Wang, Y., Zhang, J., Zheng, D., Ye, M., Chen, J.: A survey of large language models for code: Evolution, benchmarking, and future trends. arXiv preprint arXiv:2311.10372 (2023) · arXiv
  23. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al.: Palm: Scaling lan- guage modeling with pathways. Journal of Machine Learning Research 24(240), 1-113 (2023)
  24. Arora, S., Narayan, A., Chen, M.F., Orr, L., Guha, N., Bhatia, K., Chami, I., Re, C.: Ask me anything: A simple strategy for prompting language models. In: The Eleventh International Conference on Learning Representations (2022)
  25. Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., Xiong, C.: Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint arXiv:2203.13474 (2022) · arXiv
  26. Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, W.-t., Zettlemoyer, L., Lewis, M.: Incoder: A generative model for code infilling and synthesis. arXiv preprint arXiv:2204.05999 (2022) · arXiv
  27. Chen, B., Zhang, F., Nguyen, A., Zan, D., Lin, Z., Lou, J.-G., Chen, W.: Codet: Code generation with generated tests. arXiv preprint arXiv:2207.10397 (2022) · arXiv
  28. Yadav, A., Singh, M.: Boldly going where no benchmark has gone before: Exposing bias and shortcomings in code generation evaluation. arXiv preprint arXiv:2401.03855 (2024) · arXiv
  29. Dai, J., Lu, J., Feng, Y., Ruan, R., Cheng, M., Tan, H., Guo, Z.: Mhpp: Exploring the capabilities and limitations of language models beyond basic code generation. arXiv preprint arXiv:2405.11430 (2024) · arXiv
  30. Wu, Y., Liang, S., Zhang, C., Wang, Y., Zhang, Y., Guo, H., Tang, R., Liu, Y.: From human memory to ai memory: A survey on memory mechanisms in the era of llms. arXiv preprint arXiv:2504.15965 (2025) · arXiv
  31. Alfred, V.A., Monica, S.L., Jeffrey, D.U.: Compilers Principles, Techniques & Tools. Pearson Education, ??? (2007)
  32. Xiong, Y., Wang, J., Li, B., Zhu, Y., Zhao, Y.: Self-organizing agent network for llm-based workflow automation. arXiv preprint arXiv:2508.13732 (2025) · arXiv
  33. Geraci, A.: IEEE Standard Computer Dictionary: Compilation of IEEE Standard Computer Glossaries. IEEE Press, ??? (1991)
  34. Bhatt, U., Kapoor, S., Upadhyay, M., Sucholutsky, I., Quinzan, F., Collins, K.M., Weller, A., Wilson, A.G., Zafar, M.B.: When should we orchestrate multiple agents? arXiv preprint arXiv:2503.13577 (2025) · arXiv
  35. Zhang, Z., Dai, Q., Bo, X., Ma, C., Li, R., Chen, X., Zhu, J., Dong, Z., Wen, J.- R.: A survey on the memory mechanism of large language model based agents. ACM Transactions on Information Systems (2024)

Cited by

Sorted by citations

Authors
Rasheed, Z., Malik, S., Kemell, K.-K., Muhammad, W., Saari, M., Systä, K., & Abrahamsson, P.
Affiliations
Tampere University (FI)
Venue
Research Square
Publisher
Elsevier BV
Published
2024
OpenAlex
W4403238748
Cited by
27
References
261
Citations by year
2026: 11 · 2025: 9 · 2024: 7
Type
posted content
Language
en
Open access
Licence
cc-by
Locations
  • Research Square (acceptedVersion, cc-by)
  • SSRN Electronic Journal (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • European Commission
  • Business Finland
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
Keywords
multi-agent LLM systems, automatic code generation, large-scale software development, large language models, natural language requirements, code review, code verification, test engineering, software productivity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review