Natural Language Processing for Requirements Elicitation and Analysis: A Systematic Literature Review of Techniques and Tools

Requirements engineering (RE) is critical to software development, yet ambiguous stakeholder inputs and complex dataset management often cause costly defects. This study conducts a systematic literature review (SLR) to explore how artificial intelligence (AI) techniques and natural language processing (NLP) tools enhance requirements elicitation and analysis. Through analyzing 36 primary studies, we identify 34 AI techniques, including support vector machine (SVM), random forest (RF), and BERT, alongside 25 NLP tools such as NLTK, SpaCy, and BERT4RE. These technologies enhance automation, precision, and scalability in RE. They excel in classifying requirements, extracting key abstractions, and detecting ambiguities, with techniques like RoBERTa achieving up to 97.7% F1-scores and tools like the score-based ambiguity detector and resolver (SBADR) offering 99% recall for ambiguity detection. However, key limitations include data dependency, computational demands, and challenges in handling domain-specific terminology. This synthesis extends prior elicitation-focused research, highlighting AI and NLP’s transformative potential in RE while identifying gaps in domain adaptation and computational efficiency for future exploration.