A systematic review of 50 studies identifies 69 LLM-assisted tasks in empirical software engineering, concentrated in data processing and analysis with gaps in human-centered integration and reproducibility reporting.
Large language models (LLMs) for requirements engineering (RE): A systematic literature review,
11 Pith papers cite this work. Polarity classification is still indexing.
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R2Code improves requirement-to-code traceability with a bidirectional alignment network, self-reflective consistency verification, and dynamic context-adaptive retrieval, yielding 7.4% average F1 gain and up to 41.7% lower token use on five datasets.
BT-APE automates prompt engineering for requirements classification using backtracking search and dynamic examples, matching PE2 accuracy while using 72% fewer tokens and 66% less time than that baseline.
A prompt-refinement-filtering LLM pipeline is reported to catch 93-96% of impacted requirements while asking engineers to review only 2-9% of the requirement set.
A systematic mapping study of 45 LLM-based RE papers identifies and characterizes 62 public datasets, revealing imbalances in open-science practices, elicitation support, and socio-technical diversity.
A clustering-based pipeline generates individual and integration-level test specifications from thousands of automotive requirements by grouping embeddings, summarizing clusters, and applying LLM calls with bounded context and standards grounding.
LLMs can detect usability content in user reviews with F-scores comparable to humans, though performance depends strongly on prompt design.
An agentic LLM pipeline extracts and translates unstructured requirements into syntactically and semantically aligned formal properties, achieving 77.8% accuracy across three scenarios.
Design-OS is a specification-driven five-stage framework for engineering system design that maintains traceability from intent to implementation and supports human-AI collaboration, demonstrated on rotary inverted pendulum control cases.
LoRA fine-tuning enables open-source LLMs such as Ministral-8B to generate requirement-based test cases at a level comparable to pre-tuned proprietary GPT-4.1 models.
citing papers explorer
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LLM-Assisted Empirical Software Engineering: Systematic Literature Review and Research Agenda
A systematic review of 50 studies identifies 69 LLM-assisted tasks in empirical software engineering, concentrated in data processing and analysis with gaps in human-centered integration and reproducibility reporting.
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R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
R2Code improves requirement-to-code traceability with a bidirectional alignment network, self-reflective consistency verification, and dynamic context-adaptive retrieval, yielding 7.4% average F1 gain and up to 41.7% lower token use on five datasets.
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BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification
BT-APE automates prompt engineering for requirements classification using backtracking search and dynamic examples, matching PE2 accuracy while using 72% fewer tokens and 66% less time than that baseline.
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LLM-Driven Cost-Effective Requirements Change Impact Analysis
A prompt-refinement-filtering LLM pipeline is reported to catch 93-96% of impacted requirements while asking engineers to review only 2-9% of the requirement set.
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Characterizing Datasets for LLM-based Requirements Engineering: A Systematic Mapping Study
A systematic mapping study of 45 LLM-based RE papers identifies and characterizes 62 public datasets, revealing imbalances in open-science practices, elicitation support, and socio-technical diversity.
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Cluster-Aware Dual-Level Test Specification Generation for Large-Scale Automotive Software Requirements
A clustering-based pipeline generates individual and integration-level test specifications from thousands of automotive requirements by grouping embeddings, summarizing clusters, and applying LLM calls with bounded context and standards grounding.
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User Reviews as a Source for Usability Requirements: A Precursor Study on Using Large Language Models
LLMs can detect usability content in user reviews with F-scores comparable to humans, though performance depends strongly on prompt design.
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Towards an Agentic LLM-based Approach to Requirement Formalization from Unstructured Specifications
An agentic LLM pipeline extracts and translates unstructured requirements into syntactically and semantically aligned formal properties, achieving 77.8% accuracy across three scenarios.
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Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case
Design-OS is a specification-driven five-stage framework for engineering system design that maintains traceability from intent to implementation and supports human-AI collaboration, demonstrated on rotary inverted pendulum control cases.
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An empirical study of LoRA-based fine-tuning of large language models for automated test case generation
LoRA fine-tuning enables open-source LLMs such as Ministral-8B to generate requirement-based test cases at a level comparable to pre-tuned proprietary GPT-4.1 models.
- Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations