Financial regulatory documents are characterized by their fine-grained complexity and pronounced heterogeneity, featuring specialized domain-specific content and diverse structural formats that vary across different regulatory frameworks and jurisdictions. These characteristics challenge modern Question-Answering (QA) systems, which often suffer from limited domain adaptation, poor interpretability, and hallucinatory problems. This work was conducted within a banking and software company, where such challenges directly impact regulatory compliance efforts. Our goal is to introduce RegulQA, a hybrid QA system that can extract accurate, logical, and comprehensible answers from unstructured regulatory documents. RegulQA integrates knowledge graph reasoning, semantic search, and retrieval-augmented generation using large language models. Experimental evaluation shows that RegulQA improves QA performance and significantly reduces hallucination rates. The baseline model employing only the LLM demonstrated a hallucination rate of 24%, whereas the proposed approach, combining knowledge graph reasoning and semantic retrieval with LLM reasoning, effectively reduced the hallucination rate to approximately the half. This integrated approach also yielded the best balanced overall scores across key qualitative attributes, including coverage, non-redundancy, readability, and response quality.
Amal Ben Abdelhafidh, Montassar Ben Messaoud, M. Tounsi et al.· Annual International Compute...· 0 citations
Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.
Aymen Masmoudi, Belhassen Khefacha, Andrew Haralambous et al.· Annual International Compute...· 0 citations
Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.
Aymen Masmoudi, Belhassen Khefacha, Andrew Haralambous et al.· Annual International Compute...· 0 citations
