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arxiv: 2605.18684 · v1 · pith:RADUV6NVnew · submitted 2026-05-18 · 💻 cs.SE · cs.AI

Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents

Sanderson Oliveira de Macedo , Ronaldo Martins da Costa This is my paper

Pith reviewed 2026-05-20 08:54 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords reverse engineeringlegacy systemsAI agentsoperational specificationsmulti-agent pipelinetraceabilityconfidence markingdocumentation engineering
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The pith

Reversa turns legacy code into traceable operational specifications using a multi-agent pipeline that marks confidence levels and preserves gaps for human review.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Reversa as a framework for reverse documentation engineering that converts legacy software into specifications usable by AI coding agents. It structures the work as a pipeline of specialized agents that map code surfaces, analyze modules, pull out implicit rules, synthesize architecture views, generate unit specifications, and review claims for accuracy. Traceability links each specification back to source code locations, while explicit confidence scores and documented gaps keep human oversight in the loop rather than assuming perfect automation. This matters for AI agents because they need reliable context and behavioral contracts to change real systems safely, especially when business rules sit hidden inside old codebases. The authors demonstrate the approach through an exploratory migration of an ATM system from COBOL to Go but note that final parity validation was left for later work.

Core claim

Reversa organizes the reverse documentation process as a multi-agent pipeline that produces traceable operational specifications for AI agents, with explicit confidence marking and preservation of gaps for human validation. The pipeline includes agents for project surface mapping, module analysis, implicit rule extraction, architecture synthesis, unit-level specification writing, and claim review. Traceability connects generated claims directly to code locations, confidence indices flag reliability, and gaps are registered rather than auto-filled. In the reported case study on an ATM system, the pipeline produced 517 claims, 10 registered gaps, 53 Gherkin parity scenarios, and advanced nine

What carries the argument

The multi-agent pipeline of specialized agents for surface mapping, module analysis, implicit rule extraction, architecture synthesis, unit specification writing, and claim review, supported by traceability links, confidence marking, and explicit gap preservation.

If this is right

  • Traceable specifications let AI agents reference exact code origins when proposing changes, reducing risk of unintended side effects.
  • Confidence marking allows teams to focus human validation on lower-scoring claims first.
  • Registered gaps prevent AI agents from acting on incomplete knowledge by surfacing areas that need manual input.
  • The Node.js CLI with SHA-256 manifest supports safe installation and updates across different agent engines.
  • The proposed evaluation metrics for coverage, traceability, confidence, utility, and cost provide a structured way to compare future reverse documentation methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the pipeline generalizes beyond the COBOL case, organizations could use it to create AI-usable contracts for other legacy languages without full manual re-documentation.
  • The gap-preservation mechanism could be extended to track how human-filled gaps affect downstream AI modification success rates.
  • Connecting Reversa-style pipelines to automated testing frameworks might create closed-loop validation where generated specs are immediately checked against running systems.
  • This method could lower the barrier for incremental modernization by giving AI agents operational contracts instead of raw code dumps.

Load-bearing premise

The multi-agent pipeline can reliably extract implicit rules and synthesize accurate specifications from legacy code without introducing critical errors or omissions.

What would settle it

Completing the parity validation and cutover in the ATM case study and finding substantial mismatches between the generated specifications and the original system's observed behavior would show the pipeline does not produce sufficiently accurate results.

Figures

Figures reproduced from arXiv: 2605.18684 by Ronaldo Martins da Costa, Sanderson Oliveira de Macedo.

Figure 1
Figure 1. Figure 1: Conceptual pipeline of Reversa. The legacy system is analyzed by specialized agents; [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Closed loop in Reversa: discovery recovers legacy knowledge, migration transforms it [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Instantiation of the Reversa pipeline in the ATM case study. The COBOL legacy system [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
read the original abstract

Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration, and maintenance practices. At the same time, language-model-based coding agents depend on reliable context, correctness criteria, and behavioral contracts to modify real systems with lower risk. This paper presents Reversa, a reverse documentation engineering framework for converting legacy software into traceable operational specifications for AI agents. Reversa organizes this process as a multi-agent pipeline: specialized agents map the project surface, analyze modules, extract implicit rules, synthesize architecture, write unit-level specifications, and review generated claims. The proposal emphasizes three mechanisms: traceability between code and specification, explicit confidence marking, and preservation of gaps for human validation. The framework is distributed as a Node.js CLI, installs skills across multiple agent engines, and uses a SHA-256 manifest to preserve modified files during update or uninstall operations. In addition to the architectural description, we report an exploratory case study on migrating an ATM from COBOL to Go, in which the pipeline produced 517 claims classified by an internal confidence index, 10 registered gaps, 53 Gherkin parity scenarios, and a reconstruction plan with 9 of 11 tasks completed at inventory time. Final parity validation and cutover were not completed in this study. We do not claim broad empirical superiority; we position the contribution with respect to the literature on reverse engineering, LLM-based documentation, and software agents, and propose an evaluation protocol with metrics for coverage, traceability, confidence, utility, and cost.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper presents Reversa, a multi-agent reverse documentation engineering framework that converts legacy code into traceable operational specifications for AI agents. The framework organizes the process via specialized agents for project mapping, module analysis, rule extraction, architecture synthesis, unit specification writing, and claim review, with built-in traceability, confidence marking, and explicit gap preservation for human review. It is implemented as a Node.js CLI tool and evaluated in an exploratory case study migrating an ATM system from COBOL to Go, which produced 517 claims, 10 gaps, and 53 Gherkin scenarios; the study notes that final parity validation and cutover were not completed and makes no claim of broad empirical superiority.

Significance. If the multi-agent pipeline can be shown to reliably extract implicit rules and produce accurate specifications, the work would offer a practical bridge between legacy systems and LLM-based agents by supplying verifiable behavioral contracts. The explicit mechanisms for traceability and gap handling address a real pain point in software modernization, and the open CLI distribution plus proposed evaluation protocol (coverage, traceability, confidence, utility, cost) could support future comparative studies. Current evidence from a single incomplete case study, however, leaves the practical utility and error rates unquantified.

major comments (1)
  1. [Case study description] Case study description (and abstract): the manuscript reports that the pipeline generated 517 claims, 10 gaps, and 53 Gherkin scenarios for the ATM-to-Go migration but explicitly states that 'Final parity validation and cutover were not completed in this study.' Because no direct comparison between the synthesized specifications and the original legacy behavior was performed, there is no measurement of discrepancies, omissions, or introduced errors. This directly weakens support for the central claim that the multi-agent pipeline reliably extracts implicit rules without critical errors or omissions.
minor comments (2)
  1. [Framework architecture] The description of the six specialized agents would benefit from a table or diagram showing their inputs, outputs, and hand-off points to make the pipeline flow easier to follow.
  2. [Case study] The reconstruction plan is said to have 9 of 11 tasks completed; listing the two incomplete tasks and their status would clarify the scope of the reported results.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment below and have made revisions to better clarify the exploratory nature of the case study.

read point-by-point responses

  1. Referee: Case study description (and abstract): the manuscript reports that the pipeline generated 517 claims, 10 gaps, and 53 Gherkin scenarios for the ATM-to-Go migration but explicitly states that 'Final parity validation and cutover were not completed in this study.' Because no direct comparison between the synthesized specifications and the original legacy behavior was performed, there is no measurement of discrepancies, omissions, or introduced errors. This directly weakens support for the central claim that the multi-agent pipeline reliably extracts implicit rules without critical errors or omissions.

    Authors: We agree that the case study does not include direct parity validation or measurement of discrepancies against the original legacy behavior, as the manuscript already states explicitly in the abstract and case study section. However, the paper does not advance a central claim that the pipeline 'reliably extracts implicit rules without critical errors or omissions.' It is positioned as an exploratory case study that demonstrates the Reversa framework's application, producing 517 claims, 10 gaps, and 53 Gherkin scenarios, while noting that validation and cutover were not completed and making no claim of broad empirical superiority. The primary contribution is the multi-agent pipeline design with traceability, confidence scoring, and explicit gap handling. To address the concern and prevent misinterpretation, we will revise the abstract and case study section to add a dedicated limitations discussion that emphasizes the exploratory scope, the absence of validation metrics, and plans for future empirical studies on accuracy and error rates. revision: yes

Circularity Check

0 steps flagged

No significant circularity in framework description or case study

full rationale

The paper describes Reversa as a multi-agent pipeline for converting legacy code into traceable specifications, emphasizing traceability, confidence marking, and gap preservation. It reports an exploratory case study producing 517 claims, 10 gaps, and 53 Gherkin scenarios from an ATM-to-Go migration, with explicit note that final parity validation and cutover were not completed. No mathematical derivations, equations, fitted parameters, or self-referential definitions appear in the provided text. Claims rest on the described process and observed outputs rather than any reduction to inputs by construction. The contribution is positioned with respect to existing literature on reverse engineering and LLM-based documentation without load-bearing self-citations that would make the central premise circular. This is a standard descriptive framework paper with an incomplete exploratory study, self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the domain assumption that legacy code contains extractable implicit rules and on the introduction of a new multi-agent architecture without independent falsifiable evidence beyond the single exploratory study.

axioms (1)
  • domain assumption Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration, and maintenance practices.
    This premise is stated in the opening of the abstract and is required for the reverse-engineering goal to be meaningful.
invented entities (1)
  • Multi-agent pipeline consisting of specialized agents for project mapping, module analysis, rule extraction, architecture synthesis, unit specification writing, and claim review. no independent evidence
    purpose: To automate conversion of legacy code into operational specifications while maintaining traceability and flagging gaps.
    The pipeline is introduced as the core mechanism of Reversa; no independent evidence outside the exploratory case study is provided.

pith-pipeline@v0.9.0 · 5830 in / 1498 out tokens · 64926 ms · 2026-05-20T08:54:58.105409+00:00 · methodology

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Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages · 5 internal anchors

  1. [1]

    Advancing requirements engineering through generative ai: Assessing the role of llms

    Chetan Arora, John Grundy, and Mohamed Abdelrazek. Advancing requirements engineering through generative ai: Assessing the role of llms. Generative AI for Effective Software Development, 2024. doi:10.1007/978-3-031-55642-5_6

    work page doi:10.1007/978-3-031-55642-5_6 2024

  2. [2]

    Contemporary software modernization: Perspectives and challenges to deal with legacy systems, 2024

    Wesley Klewerton Guez Assuncao, Luciano Marchezan, Alexander Egyed, and Rudolf Ramler. Contemporary software modernization: Perspectives and challenges to deal with legacy systems, 2024. doi:10.48550/arXiv.2407.04017

    work page doi:10.48550/arxiv.2407.04017 2024

  3. [3]

    Knowledgegraphbasedrepository-levelcodegeneration

    MihirAthaleandVishalVaddina. Knowledgegraphbasedrepository-levelcodegeneration. In 2025 IEEE/ACM International Workshop on Large Language Models for Code (LLM4Code), pages 169–176, 2025. doi:10.1109/LLM4Code66737.2025.00026

    work page doi:10.1109/llm4code66737.2025.00026 2025

  4. [4]

    Program Synthesis with Large Language Models

    Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. Program synthesis with large language models, 2021. doi:10.48550/arXiv.2108.07732

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2108.07732 2021

  5. [5]

    AutoReSpec: A Framework for Generating Specification using Large Language Models

    Ragib Shahariar Ayon and Shibbir Ahmed. AutoReSpec: A framework for generating specification using large language models, 2026. doi:10.48550/arXiv.2604.03758

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2604.03758 2026

  6. [6]

    Addison- Wesley Professional, 4 edition, 2021

    Len Bass, Paul Clements, and Rick Kazman.Software Architecture in Practice. Addison- Wesley Professional, 4 edition, 2021

    work page 2021

  7. [7]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, 14 Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mo- hammad Bav...

    work page

  8. [8]

    doi:10.48550/arXiv.2107.03374

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2107.03374

  9. [9]

    Knowledgegraphbasedrepository-levelcodegeneration

    Nilesh Dhulshette, Sapan Shah, and Vinay Kulkarni. Hierarchical repository-level code summarization for business applications using local llms. In2025 IEEE/ACM International Workshop on Large Language Models for Code (LLM4Code), pages 145–152, 2025. doi: 10.1109/LLM4Code66737.2025.00023

    work page doi:10.1109/llm4code66737.2025.00023 2025

  10. [10]

    Brunelle, and Samruddhi Thaker

    Colin Diggs, Michael Doyle, Amit Madan, Siggy Scott, Emily Escamilla, Jacob Zimmer, Naveed Nekoo, Paul Ursino, Michael Bartholf, Zachary Robin, Anand Patel, Chris Glasz, William Macke, Paul Kirk, Jasper Phillips, Arun Sridharan, Doug Wendt, Scott Rosen, Nitin Naik, Justin F. Brunelle, and Samruddhi Thaker. Leveraging llms for legacy code modernization: Ch...

    work page doi:10.48550/arxiv.2411.14971 2024

  11. [11]

    URL: https://proceedings.neurips.cc/paper_files/paper/2024/hash/ c2ce2f2701c10a2b2f2ea0bfa43cfaa3-Abstract-Conference.html

    Hao Ding, Ziwei Fan, Ingo Guehring, Gaurav Gupta, Wooseok Ha, Jun Huan, Linbo Liu, Behrooz Omidvar-Tehrani, Shiqi Wang, and Hao Zhou. Reasoning and planning with large language models in code development. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 6480–6490, 2024. doi:10.1145/3637528. 3671452

    work page doi:10.1145/3637528 2024

  12. [12]

    Leveraging large language models for use case model generation from software requirements

    Tobias Eisenreich, Nicholas Friedlaender, and Stefan Wagner. Leveraging large language models for use case model generation from software requirements. In2025 40th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW), pages 221–227, 2025. doi:10.1109/ASEW67777.2025.00050

    work page doi:10.1109/asew67777.2025.00050 2025

  13. [13]

    Muhammet Kursat Gormez, Murat Yilmaz, and Paul M. Clarke. Large language models for software engineering: A systematic mapping study. InSoftware Process Improvement and Capability Determination, pages 78–93. Springer, 2024. doi:10.1007/978-3-031-71139-8_ 5

    work page doi:10.1007/978-3-031-71139-8_ 2024

  14. [14]

    Large language models for software engineering: A systematic literature review

    Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. Large language models for software engineering: A systematic literature review.ACM Transactions on Software Engineering and Methodology, 33(8):1–79, 2024. doi:10.1145/3695988

    work page doi:10.1145/3695988 2024

  15. [15]

    Iso/iec/ieee 42010:2022, software, systems and enterprise – architecture description, 2022

    ISO/IEC/IEEE. Iso/iec/ieee 42010:2022, software, systems and enterprise – architecture description, 2022

    work page 2022

  16. [16]

    SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

    Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. SWE-bench: Can language models resolve real-world github issues? InThe Twelfth International Conference on Learning Representations, 2024. doi: 10.48550/arXiv.2310.06770

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2310.06770 2024

  17. [17]

    Morescient gai for software engineering, 2024

    Marcus Kessel and Colin Atkinson. Morescient gai for software engineering, 2024. doi: 10.48550/arXiv.2406.04710. 15

    work page doi:10.48550/arxiv.2406.04710 2024

  18. [18]

    CEGen: Cause-effect graph generation using large language models

    Hiroyuki Kirinuki. CEGen: Cause-effect graph generation using large language models. In 2024 31st Asia-Pacific Software Engineering Conference (APSEC), pages 521–522, 2024. doi:10.1109/APSEC65559.2024.00073

    work page doi:10.1109/apsec65559.2024.00073 2024

  19. [19]

    Using llms in software requirements specifications: An empirical evaluation

    Madhava Krishna, Bhagesh Gaur, Arsh Verma, and Pankaj Jalote. Using llms in software requirements specifications: An empirical evaluation. In2024 IEEE 32nd International Requirements Engineering Conference (RE), pages 475–483, 2024. doi:10.1109/RE59067. 2024.00056

    work page doi:10.1109/re59067 2024

  20. [20]

    Reproducible, explainable, and effective evaluations of agentic ai for software engineering, 2026

    Jingyue Li and Andre Storhaug. Reproducible, explainable, and effective evaluations of agentic ai for software engineering, 2026. doi:10.48550/arXiv.2604.01437

    work page doi:10.48550/arxiv.2604.01437 2026

  21. [21]

    Large language model-based agents for software engineering: A survey.ACM Transactions on Software Engineering and Methodology, 2026

    Junwei Liu, Kaixin Wang, Yixuan Chen, Xin Peng, Zhenpeng Chen, Lingming Zhang, and Yiling Lou. Large language model-based agents for software engineering: A survey.ACM Transactions on Software Engineering and Methodology, 2026. doi:10.1145/3796507

    work page doi:10.1145/3796507 2026

  22. [22]

    RAG-driven multiple assertions generation with large language models.Empirical Software Engineering, 30(4), 2025

    Zhuang Liu, Hailong Wang, Tongtong Xu, and Bei Wang. RAG-driven multiple assertions generation with large language models.Empirical Software Engineering, 30(4), 2025. doi: 10.1007/s10664-025-10641-1

    work page doi:10.1007/s10664-025-10641-1 2025

  23. [23]

    RepoAgent: An llm-powered open-source framework for repository-level code documentation generation,

    Qinyu Luo, Yining Ye, Shihao Liang, Zhong Zhang, Yujia Qin, Yaxi Lu, Yesai Wu, Xin Cong, Yankai Lin, Yingli Zhang, Xiaoyin Che, Zhiyuan Liu, and Maosong Sun. RepoAgent: An llm-powered open-source framework for repository-level code documentation generation,

    work page

  24. [24]

    doi:10.48550/arXiv.2402.16667

    work page doi:10.48550/arxiv.2402.16667

  25. [25]

    Testingthe effect of code documentation on large language model code understanding

    William Macke and Michael Doyle. Testingthe effect of code documentation on large language model code understanding. InFindings of the Association for Computational Linguistics: NAACL 2024, pages 1044–1050, 2024. doi:10.18653/v1/2024.findings-naacl.66

    work page doi:10.18653/v1/2024.findings-naacl.66 2024

  26. [26]

    Integrating graph retrieval augmented generation method with large language models to improve software requirement specification, 2024

    Arsalan Masoudifard, Mohammad Mowlavi Sorond, Moein Madadi, Mohammad Sabokrou, and Elahe Habibi. Integrating graph retrieval augmented generation method with large language models to improve software requirement specification, 2024. doi:10.2139/ssrn. 4961380

    work page doi:10.2139/ssrn 2024

  27. [27]

    Consciousness and Cognition20(2011) https://doi.org/10.1016/j

    Thakshila Imiya Mohottige, Artem Polyvyanyy, Colin J. Fidge, Rajkumar Buyya, and Alistair Barros. Reengineering software systems into microservices: State-of-the-art and future directions.Information and Software Technology, 183:107732, 2025. doi:10.1016/j. infsof.2025.107732

    work page doi:10.1016/j 2025

  28. [28]

    C hat D ev: Communicative Agents for Software Development

    Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. ChatDev: Communicative agents for software development. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15174–15186, 20...

    work page doi:10.18653/v1/2024.acl-long.810 2024

  29. [29]

    XIS-Reverse: A model-driven reverse engineering approach for legacy information systems

    Andre Reis and Alberto Rodrigues da Silva. XIS-Reverse: A model-driven reverse engineering approach for legacy information systems. InProceedings of the 5th International Conference on Model-Driven Engineering and Software Development, pages 196–207, 2017. doi:10. 5220/0006271501960207

    work page 2017

  30. [30]

    Robert, Ipek Ozkaya, and Douglas C

    John E. Robert, Ipek Ozkaya, and Douglas C. Schmidt. Transforming software engineering and software acquisition with large language models. Artificial Intelligence and Large Language Models, 2026. doi:10.1201/9781003492252-7

    work page doi:10.1201/9781003492252-7 2026

  31. [31]

    The future of ai-driven software engineering, 2024

    Valerio Terragni, Annie Vella, Partha Roop, and Kelly Blincoe. The future of ai-driven software engineering, 2024. doi:10.48550/arXiv.2406.07737. 16

    work page doi:10.48550/arxiv.2406.07737 2024

  32. [32]

    The impact of traceability on software maintenance and evolution: A mapping study

    Fangchao Tian, Tianlu Wang, Peng Liang, Chong Wang, Arif Ali Khan, and Muhammad Ali Babar. The impact of traceability on software maintenance and evolution: A mapping study. Journal of Software: Evolution and Process, 33(10), 2021. doi:10.1002/smr.2374

    work page doi:10.1002/smr.2374 2021

  33. [33]

    Software architecture in practice: Challenges and opportunities

    Zhiyuan Wan, Yun Zhang, Xin Xia, Yi Jiang, and David Lo. Software architecture in practice: Challenges and opportunities. InProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 1457–1469, 2023. doi:10.1145/3611643.3616367

    work page doi:10.1145/3611643.3616367 2023

  34. [34]

    Learning from failure: Integrating negative examples when fine-tuning large language models as agents,

    Renxi Wang, Haonan Li, Xudong Han, Yixuan Zhang, and Timothy Baldwin. Learning from failure: Integrating negative examples when fine-tuning large language models as agents,

    work page

  35. [35]

    doi:10.48550/arXiv.2402.11651

    work page doi:10.48550/arxiv.2402.11651

  36. [36]

    Advancements and challenges of large language model-based code generation and completion

    Zheer Wang. Advancements and challenges of large language model-based code generation and completion. InProceedings of the 1st International Conference on Modern Logistics and Supply Chain Management, pages 208–213, 2024. doi:10.5220/0013271800004558

    work page doi:10.5220/0013271800004558 2024

  37. [37]

    KerSpecGen: Co-piloting formal kernel specification synthesis with refined knowledge graphs and large lan- guage models.PLOS ONE, 20(12):e0338821, 2025

    Zi Wang, Xiaoyu Zhu, Hongqiang Wang, Yichun Yu, and Yuqing Lan. KerSpecGen: Co-piloting formal kernel specification synthesis with refined knowledge graphs and large lan- guage models.PLOS ONE, 20(12):e0338821, 2025. doi:10.1371/journal.pone.0338821

    work page doi:10.1371/journal.pone.0338821 2025

  38. [38]

    ProMeTA: A taxonomy for program metamodels in program reverse engineering.Empirical Software Engineering, 23:2323–2358, 2018

    Hironori Washizaki, Yann-Gaël Guéhéneuc, and Foutse Khomh. ProMeTA: A taxonomy for program metamodels in program reverse engineering.Empirical Software Engineering, 23:2323–2358, 2018. doi:10.1007/s10664-017-9592-3

    work page doi:10.1007/s10664-017-9592-3 2018

  39. [39]

    Prompt engineering with ai coding agents

    Nick Wienholt. Prompt engineering with ai coding agents. GitHub Copilot and AI Coding Tools in Practice, 2025. doi:10.1007/979-8-8688-1784-7_4

    work page doi:10.1007/979-8-8688-1784-7_4 2025

  40. [40]

    Danning Xie, Byungwoo Yoo, Nan Jiang, Mijung Kim, Lin Tan, Xiangyu Zhang, and Judy S. Lee. How effective are large language models in generating software specifications? In 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2025. doi:10.1109/SANER64311.2025.00014

    work page doi:10.1109/saner64311.2025.00014 2025

  41. [41]

    emnlp-main.394/

    John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. SWE-agent: Agent-computer interfaces enable automated software engineering. InAdvances in Neural Information Processing Systems 37, pages 50528–50652, 2024. doi:10.52202/079017-1601

    work page doi:10.52202/079017-1601 2024

  42. [42]

    RepoCBench: A benchmark for c-oriented repository-level code generation with large language models and agents, 2025

    YuBo Zhang, KaiChun Yao, LiBo Zhang, and Chen Zhao. RepoCBench: A benchmark for c-oriented repository-level code generation with large language models and agents, 2025. doi:10.2139/ssrn.5886003

    work page doi:10.2139/ssrn.5886003 2025

  43. [43]

    A Survey of Large Language Models

    Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. A survey of large language models, 2023. doi: 10.48550/arXiv.2303.18223

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2303.18223 2023

  44. [44]

    A survey of large language models for code: Evolution, benchmarking, and future trends, 2023

    Zibin Zheng, Kaiwen Ning, Yanlin Wang, Jingwen Zhang, Dewu Zheng, Mingxi Ye, and Jiachi Chen. A survey of large language models for code: Evolution, benchmarking, and future trends, 2023. doi:10.48550/arXiv.2311.10372

    work page doi:10.48550/arxiv.2311.10372 2023

  45. [45]

    T. Zhu, L. C. Cordeiro, and Y. Sun. ReqInOne: A large language model-based agent for software requirements specification generation. InProceedings of the IEEE International Conference on Requirements Engineering, pages 449–457, 2025. doi:10.1109/RE63999. 2025.00054. 17

    work page doi:10.1109/re63999 2025