Semantic API Alignment: Linking High-level User Goals to APIs (2405.04236v1)
Abstract: LLMs are becoming key in automating and assisting various software development tasks, including text-based tasks in requirements engineering but also in coding. Typically, these models are used to automate small portions of existing tasks, but we present a broader vision to span multiple steps from requirements engineering to implementation using existing libraries. This approach, which we call Semantic API Alignment (SEAL), aims to bridge the gap between a user's high-level goals and the specific functions of one or more APIs. In this position paper, we propose a system architecture where a set of LLM-powered ``agents'' match such high-level objectives with appropriate API calls. This system could facilitate automated programming by finding matching links or, alternatively, explaining mismatches to guide manual intervention or further development. As an initial pilot, our paper demonstrates this concept by applying LLMs to Goal-Oriented Requirements Engineering (GORE), via sub-goal analysis, for aligning with REST API specifications, specifically through a case study involving a GitHub statistics API. We discuss the potential of our approach to enhance complex tasks in software development and requirements engineering and outline future directions for research.
- R. Feldt, S. Kang, J. Yoon, and S. Yoo, “Towards autonomous testing agents via conversational large language models,” 2023.
- J. Yoon, R. Feldt, and S. Yoo, “Autonomous large language model agents enabling intent-driven mobile gui testing,” 2023.
- S. Das, N. Deb, A. Cortesi, and N. Chaki, “Extracting goal models from natural language requirement specifications,” Journal of Systems and Software, p. 111981, 2024.
- S. Arulmohan, M.-J. Meurs, and S. Mosser, “Extracting domain models from textual requirements in the era of large language models,” in 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023, pp. 580–587.
- G. De Vito, F. Palomba, C. Gravino, S. Di Martino, and F. Ferrucci, “Echo: An approach to enhance use case quality exploiting large language models,” in 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2023, pp. 53–60.
- A. Van Lamsweerde, “Goal-oriented requirements engineering: A guided tour,” in Proceedings fifth ieee international symposium on requirements engineering. IEEE, 2001, pp. 249–262.
- A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, and J. M. Zhang, “Large language models for software engineering: Survey and open problems,” arXiv preprint arXiv:2310.03533, 2023.
- M. Kim, D. Corradini, S. Sinha, A. Orso, M. Pasqua, R. Tzoref-Brill, and M. Ceccato, “Enhancing rest api testing with nlp techniques,” in Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2023, pp. 1232–1243.
- M. Kim, T. Stennett, D. Shah, S. Sinha, and A. Orso, “Leveraging large language models to improve rest api testing,” arXiv preprint arXiv:2312.00894, 2023.
- Robert Feldt (80 papers)
- Riccardo Coppola (11 papers)