Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.04729 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915835666235392 |
|---|---|
| author | Rathnayake, Amila Shahin, Mojtaba Abaei, Golnoush |
| author_facet | Rathnayake, Amila Shahin, Mojtaba Abaei, Golnoush |
| contents | This paper presents an evaluation of three LLMs, GPT-4, Claude 3, and Gemini, for automated Behaviour-Driven Development (BDD) scenarios generation. To support this evaluation, we constructed a dataset of 500 user stories, requirement descriptions, and their corresponding BDD scenarios, drawn from four proprietary software products. We assessed the quality of BDD scenarios generated by LLMs using a multidimensional evaluation framework encompassing text and semantic similarity metrics, LLM-based evaluation, and human expert assessment. Our findings reveal that although GPT-4 achieves higher scores in text and semantic similarity metrics, Claude 3 produces scenarios rated highest by both human experts and LLM-based evaluators. LLM-based evaluators, particularly DeepSeek, show a stronger correlation with human judgment than with text similarity and semantic similarity metrics. The effectiveness of prompting techniques is model-specific: GPT-4 performs best with zero-shot, Claude 3 benefits from chain-of-thought reasoning, and Gemini achieves optimal results with few-shot examples. Input quality determines the effectiveness of BDD scenario generation: detailed requirement descriptions alone yield high-quality scenarios, whereas user stories alone yield low-quality scenarios. Our experiments indicate that setting temperature to 0 and top_p to 1.0 produced the highest-quality BDD scenarios across all models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04729 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Behaviour Driven Development Scenario Generation with Large Language Models Rathnayake, Amila Shahin, Mojtaba Abaei, Golnoush Software Engineering This paper presents an evaluation of three LLMs, GPT-4, Claude 3, and Gemini, for automated Behaviour-Driven Development (BDD) scenarios generation. To support this evaluation, we constructed a dataset of 500 user stories, requirement descriptions, and their corresponding BDD scenarios, drawn from four proprietary software products. We assessed the quality of BDD scenarios generated by LLMs using a multidimensional evaluation framework encompassing text and semantic similarity metrics, LLM-based evaluation, and human expert assessment. Our findings reveal that although GPT-4 achieves higher scores in text and semantic similarity metrics, Claude 3 produces scenarios rated highest by both human experts and LLM-based evaluators. LLM-based evaluators, particularly DeepSeek, show a stronger correlation with human judgment than with text similarity and semantic similarity metrics. The effectiveness of prompting techniques is model-specific: GPT-4 performs best with zero-shot, Claude 3 benefits from chain-of-thought reasoning, and Gemini achieves optimal results with few-shot examples. Input quality determines the effectiveness of BDD scenario generation: detailed requirement descriptions alone yield high-quality scenarios, whereas user stories alone yield low-quality scenarios. Our experiments indicate that setting temperature to 0 and top_p to 1.0 produced the highest-quality BDD scenarios across all models. |
| title | Behaviour Driven Development Scenario Generation with Large Language Models |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2603.04729 |