Behavior Driven Development: A Systematic Literature Review

Farooq, Muhammad Shoaib; Omer, Uzma; Ramzan, Amna; Rasheed, Mansoor Ahmad; Atal, Zabihullah

doi:10.1109/access.2023.3302356

Cited by 4 publications

(1 citation statement)

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“…Despite its effectiveness in translating software specifications into behavior for validation, BDD experiences challenges in manually crafting acceptance tests, leading to a notable impact on efficiency and accuracy [4]. Ensuring highquality BDD acceptance tests is crucial for successful implementation [34].…”

Section: Scenario: Performing Additionmentioning

confidence: 99%

Comprehensive Evaluation and Insights Into the Use of Large Language Models in the Automation of Behavior-Driven Development Acceptance Test Formulation

Karpurapu,

Myneni,

Nettur

et al. 2024

IEEE Access

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Behavior-driven development (BDD) is an Agile testing methodology fostering collaboration among developers, QA analysts, and stakeholders. In this manuscript, we propose a novel approach to enhance BDD practices using large language models (LLMs) to automate acceptance test generation. Our study uses zero and few-shot prompts to evaluate LLMs such as GPT-3.5, GPT-4, Llama-2-13B, and PaLM-2. The paper presents a detailed methodology that includes the dataset, prompt techniques, LLMs, and the evaluation process. The results demonstrate that GPT-3.5 and GPT-4 generate error-free BDD acceptance tests with better performance. The few-shot prompt technique highlights its ability to provide higher accuracy by incorporating examples for in-context learning. Furthermore, the study examines syntax errors, validation accuracy, and comparative analysis of LLMs, revealing their effectiveness in enhancing BDD practices. However, our study acknowledges that there are limitations to the proposed approach. We emphasize that this approach can support collaborative BDD processes and create opportunities for future research into automated BDD acceptance test generation using LLMs.INDEX TERMS agile software development, behavior driven development, large language model, machine learning, natural language processing, prompt engineering, software testing, test cases, test automation, zero-shot, few-shot.

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Section: Scenario: Performing Additionmentioning

confidence: 99%