10.29327/1884517.29-6
Ambiguity in requirements engineering is a pervasive quality defect that can lead to misunderstandings, rework, and increased development costs. In recent years, Artificial Intelligence (AI) techniques have been increasingly explored to support the detection and analysis of ambiguity in natural language requirements. However, existing research is dispersed across different ambiguity types, AI techniques, and Requirements Engineering (RE) activities, making it difficult to obtain a consolidated view of the state of the art. This paper presents a Systematic Mapping Study (SMS) that examines AI-based approaches for addressing ambiguity in requirements engineering. Following established guidelines for systematic studies in software engineering, the literature was systematically identified, selected, and analyzed. The study provides a structured overview of existing approaches, identifies research trends and limitations, and highlights directions for future research in AI-supported requirements quality analysis.
Keywords: Ambiguity detection; Requirements Engineering; Artificial Intelligence; Systematic Mapping Study
@inproceedings{wer202605,
author = {Torres, J. I. and Antonelli, L. and Thomas, P.},
title = {Ambiguity in Requirements Engineering: A Systematic Mapping of AI-Based Approaches},
booktitle = {Anais do Workshop em Engenharia de Requisitos - Proceedings of the 29th Workshop on Requirements Engineering (WER2026)},
year = {2026},
issn = {2675-0066},
isbn = {978-65-02-19591-8},
doi = {10.29327/1884517.29-6}
}