Empirical Data Package for "Quality Assessment of Software Requirements Using Artificial Intelligence Methods: A Systematic Literature Review"

This dataset, referred to as the "Empirical Data Package", contains all systematically extracted data for the following study: E. Wolf, A. Trendowicz, J. Siebert, "Quality Assessment of Software Requirements Using Artificial Intelligence Methods: A Systematic Literature Review". Research objective: The study investigates how AI techniques and models are applied to assess and improve the quality of software requirements. It addresses three research questions: - RQ1: Which AI methods and models are used for automated assessment of requirements quality? - RQ2: Which quality aspects and associated metrics are considered for assessment? - RQ3: Which datasets are used to create, evaluate, and adjust requirements quality assessment models? Dataset contents: - Screening logs, including title-abstract review, snowballing results, and the final set of 26 primary studies. - Demographic information such as type of contribution and research context (industry vs. academia). - Quality assessment data per study, including applied criteria and scoring. - Extracted mappings for each study: AI methods, evaluation metrics, quality aspects, purposes of AI application, RE phases targeted, and datasets used. - Organized per research question (RQ1–RQ3) in Excel worksheets with grouping and content tabs. Notable findings included in the dataset: - Requirement quality aspects from multiple sources can largely be mapped to the INVEST framework. - AI models primarily focus on detecting issues; few provide actionable improvements. - AI methods rarely cover all INVEST aspects, with some quality criteria neglected. - The field exhibits heterogeneity in datasets, labeling strategies, and evaluation metrics, highlighting challenges for reproducibility, generalization, and adoption in practice. - Recent AI approaches, including GenAI models, offer opportunities for explanation and recommendations, but require careful adaptation by RE practitioners. Purpose: The dataset supports transparency, reproducibility, and further analysis of AI-based methods for software requirements quality assessment and can serve as a foundation for future benchmark datasets and standardized evaluation frameworks.