Automated Software Requirements Elicitation: A Systematic Mapping Study

Large Language Models for Requirements Engineering: A Cross-Task Empirical Evaluation

This work presents the first cross-task empirical evaluation of LLMs spanning five RE-related activities, as well as replication materials supporting reproducibility, and a broader understanding of the capabilities, limitations, and practical readiness of current LLMs for RE.

From user stories to architectural blueprints: an AI-augmented NLP pipeline for large-scale agile software engineering

Automated LLM-based Classification of Software Requirements

The adoption of large language models (LLMs) in software engineering has enabled the potential to automate complex activities such as requirements analysis. This paper presents an empirical performance analysis of four modern LLMs: GPT-4o, Aya, Gemma and Phi-4 on the task of automated classification of atomic software requirements. The LLMs were invoked under two different scenarios to solve a multilabel classification of 296 requirements extracted from the PROMISE[Formula: see text] dataset. The baseline scenario relies solely on internal model knowledge, whereas the rubric-augmented scenario uses formal definitions derived from the SQuaRE product quality model. The results indicate that GPT-4o consistently attains the highest overall classification accuracy under both scenarios. Moreover, all LLMs exhibit strong and stable performance in identifying functional, performance efficiency and security requirements. Inter-rater agreement assessments using Cohen’s and Fleiss’ Kappa coefficients further demonstrate moderate to substantial agreement among the outputs of the evaluated LLMs.

Artificial Intelligence Tools for Productivity Enhancement: A Systematic Literature Review 

Artificial Intelligence (AI), particularly generative AI based on Large Language Models (LLMs), has rapidly transformed the execution of knowledge-intensive activities across multiple domains. AI-powered tools such as ChatGPT, GitHub Copilot, Microsoft Copilot, Google Gemini, Claude, and Notion AI have increasingly been adopted to automate repetitive tasks, support decision-making, accelerate software development, and improve content production. However, despite the rapid expansion of these technologies, scientific evidence regarding their effectiveness as productivity enhancers remains distributed across different research areas. This study presents a systematic literature review aimed at synthesizing current evidence on the role of AI tools in improving productivity in business, education, software engineering, and scientific research. A structured literature search was conducted across major academic databases, including Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Studies published between 2020 and 2026 were analyzed according to predefined inclusion and exclusion criteria. The reviewed literature indicates that AI tools can improve productivity by reducing task completion time, assisting knowledge creation, supporting programming activities, and enhancing information processing. Nevertheless, significant challenges remain, including inaccurate outputs, algorithmic bias, privacy concerns, ethical risks, and excessive reliance on automated systems. The findings suggest that AI achieves the greatest productivity benefits when used as a collaborative technology that augments human capabilities rather than replacing human expertise. Future research should investigate long-term productivity impacts, organizational adaptation strategies, responsible AI governance, and the integration of advanced multimodal AI systems into professional workflows.

From Natural Language Requirements to Graphical User Interfaces: Automated Prototyping and Verification with Pretrained Language Models

This work proposes LLM-based methods for verifying semantically complex NL requirements on static GUI prototypes and introduces a multimodal LLM-based agent for verifying complex functional and non-functional requirements in dynamic GUI applications through automatically generated and evaluated interaction trajectories.

Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring

Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model. Methods: We present a neuro-symbolic multi-agent architecture that operationalizes the Object-Oriented Method for Requirements Authoring and Management (OOMRAM) lattice. The LLM acts as a non-deterministic heuristic for lattice traversal, while a deterministic symbolic validator enforces all structural constraints. We introduce a three-valued (T, I, F) -- Truth, Indeterminacy, Falsity -- framework to classify and score the LLM's requirement decisions before and after validation. Results: Evaluated across 37 natural-language project visions in eleven application families, the system completely eliminated structural inconsistencies in 35 out of 37 cases (94.6%), with the remaining two containing only 6 unresolved structural errors (0.39% of decisions) due to iteration limits. Three-valued analysis revealed that 24.7% of all decisions are indeterminate -- structurally valid but discretionary choices not explicitly mandated by the stakeholder. Conclusion: Offloading structural integrity to a deterministic symbolic layer successfully guarantees structural conformance, while the three-valued classification provides a formal way to measure neural uncertainty, facilitating safe LLM deployment in formal requirements engineering.