Quality assessment of software requirements using artificial intelligence methods: A systematic literature review

Context: The quality of requirements specifications is a critical success factor in software development. Assuring high-quality requirements, specifically in an automated way, poses a significant challenge due to their unstructured and multi-modal character. With the rise of deep learning and large language models (LLMs), new opportunities have developed to assess the quality of requirements automatically, particularly user stories in the context of agile software engineering, where short development cycles require efficient tool support. Objective: This study aims to systematically review and investigate the current landscape of approaches based on artificial intelligence techniques such as natural language processing and deep learning for assessing the quality of software requirements. The investigation focuses on the artificial intelligence techniques adopted, quality aspects considered, datasets used to tune and evaluate the proposed approaches, and their performance. Method: We conducted a systematic literature review of 26 peer-reviewed papers published between 2019 and 2025. We selected the papers after a title and abstract review of 353 papers identified through a literature databases query and forward–backward snowballing. Results: The results reveal significant overlap among considered quality aspects, which can be mapped onto the higher-order requirements quality model INVEST. Most studies focus on assessing requirement quality rather than improving requirements and rely heavily on synthetic and public datasets. LLMs have rapidly gained popularity since 2023, though model evaluation strategies remain inconsistent. Metrics such as accuracy, precision, recall, and F1-Score are common, yet a few studies use semantic or expert-based evaluations. Conclusion: The field is evolving toward LLM-driven, semantically rich models, yet lacks methodological standardization, reproducible datasets for evaluating the models, and integration of the approaches with real-world requirements engineering processes. Future work should address these limitations by developing benchmark datasets, standardizing evaluation metrics, and exploring hybrid systems that combine AI-based and traditional requirements quality assurance approaches.