End-to-End Documentation Automation Using AI and REST APIs in Enterprise Collaboration Platforms
Keywords:
API, AI, RESTful API, NLPAbstract
In modern software development environments, maintaining accurate and up-to-date project documentation remains a persistent challenge due to rapid iteration cycles and distributed team structures. This paper presents a novel AI-powered Auto-Documentation Bot designed to autonomously generate, structure, and publish project documentation directly to Confluence by leveraging natural language processing (NLP) and RESTful API integration. The proposed system aggregates information from heterogeneous sources such as Git repositories, JIRA tickets, and team communication platforms, and applies transformer-based models to extract key insights and generate semantically coherent summaries. A context-sensitive template generator dynamically organizes the extracted content into structured documentation formats, eliminating the need for static templates. Furthermore, an optional human-in-the-loop module enables expert validation before final publication, with feedback integrated into a continual learning loop that refines the NLP pipeline over time. Experimental deployment demonstrates significant reductions in manual effort and improved documentation consistency, suggesting that AI-assisted documentation systems can transform how teams manage and disseminate technical knowledge
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R. Robillard, B. Dagenais, and F. Fleurey, “Software Documentation: A Systematic Literature Review and Research Directions,” ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 22, no. 1, pp. 1–38, Jan. 2013.
A. Moreno, G. Sillitti, and G. Succi, “An Empirical Study of the Relationship between Code Documentation and Software Quality,” IEEE Transactions on Software Engineering, vol. 39, no. 10, pp. 1384–1396, Oct. 2013.
M. Allamanis, E. T. Barr, P. Devanbu, and C. Sutton, “A Survey of Machine Learning for Big Code and Naturalness,” ACM Computing Surveys, vol. 51, no. 4, pp. 1–37, Jul. 2018.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep Code Comment Generation with Hybrid Lexical and Syntactical Information,” in Proceedings of the 26th IEEE/ACM International Conference on Program Comprehension (ICPC), 2018, pp. 119–130.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL-HLT, 2019, pp. 4171–4186.
C. Raffel et al., “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,” Journal of Machine Learning Research, vol. 21, no. 140, pp. 1–67, 2020.
Y. Liu et al., “Fine-tune BERT for Extractive Summarization,” in Proceedings of ACL, 2019, pp. 2335–2345.
D. Lee, Y. He, and M. S. Chen, “Named Entity Recognition using Deep Learning: A Survey,” ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 1, Article 6, 2023.
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