# Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation

arXiv: https://arxiv.org/abs/2602.04726 · HTML: https://mlsanity.com/paper/2602.04726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 21 of 21)

- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [CoIR: A Comprehensive Benchmark for Code Information Retrieval Models](https://mlsanity.com/paper/2407.02883/) (2024)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025)
- [Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community](https://mlsanity.com/paper/2307.09751/) (2023)
- [FanOutQA: A Multi-Hop, Multi-Document Question Answering Benchmark for Large Language Models](https://mlsanity.com/paper/2402.14116/) (2024)
- [Semantically Enhanced Software Traceability Using Deep Learning Techniques](https://mlsanity.com/paper/1804.02438/) (2018)
- [System for systematic literature review using multiple AI agents: Concept and an empirical evaluation](https://mlsanity.com/paper/2403.08399/) (2024)
- [Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study](https://mlsanity.com/paper/2404.12772/) (2024)
- [Conceptual Framework for Autonomous Cognitive Entities](https://mlsanity.com/paper/2310.06775/) (2023)
- [Automatic High-Level Test Case Generation using Large Language Models](https://mlsanity.com/paper/2503.17998/) (2025)
- [A Survey of Long-Document Retrieval in the PLM and LLM Era](https://mlsanity.com/paper/2509.07759/) (2025)
- [Retrieval for Extremely Long Queries and Documents with RPRS: a Highly Efficient and Effective Transformer-based Re-Ranker](https://mlsanity.com/paper/2303.01200/) (2023)
- [A Tool for Test Case Scenarios Generation Using Large Language Models](https://mlsanity.com/paper/2406.07021/) (2024)
- [Scenario-Guided LLM-based Mobile App GUI Testing](https://mlsanity.com/paper/2506.05079/) (2025)
- [The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval](https://mlsanity.com/paper/2111.09852/) (2021)
- [Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models](https://mlsanity.com/paper/2506.00773/) (2025)
- [An Empirical study on LLM-based Log Retrieval for Software Engineering Metadata Management](https://mlsanity.com/paper/2506.11659/) (2025)

## Abstract (arXiv)

The introduction of large language models ignited great retooling and rethinking of the software development models. The ensuing response of software engineering research yielded a massive body of tools and approaches. In this paper, we join the hassle by introducing agentic AI solutions for two tasks. First, we developed a solution for automatic test scenario generation from a detailed requirements description. This approach relies on specialized worker agents forming a star topology with the supervisor agent in the middle. We demonstrate its capabilities on a real-world example. Second, we developed an agentic AI solution for the document retrieval task in the context of software engineering documents. Our solution enables performing various use cases on a body of documents related to the development of a single software, including search, question answering, tracking changes, and large document summarization. In this case, each use case is handled by a dedicated LLM-based agent, which performs all subtasks related to the corresponding use case. We conclude by hinting at the future perspectives of our line of research.