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Abstract Companies adopt agile methodologies for various reasons, primarily due to their adaptability to change and evolving business demands. In this context, addressing non-functional requirements (NFRs) may not always be a priority and can present challenges for agile teams. The focus on User Stories present in agile methods and tools often does not offer explicit alternatives for documenting NFRs. In this research, we perform a survey to explore five different strategies for documenting NFRs, to identify which fits better for different types of quality attributes and to understand the strengths and drawbacks of each one. As a result, the participants considered certain strategies as being more or less suitable for specifying different types of quality attributes. For instance, while Story Labeling was rarely recommended for security requirements, using Story Sub-sections or Verification Rules were highly recommended for this kind of quality attribute. Our results also evaluated the strategies considering several factors, such as the level of detail and requirement duplication. As a practical implication, the results of this work can provide guidance to agile development teams in choosing the most suitable alternative for each NFR documentation.

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Abstract Generative AI has been rapidly adopted by the software development industry in various ways, offering innovative approaches to transforming requirements into working software. Combining Generative AI with Test-Driven Development (TDD) presents a creative method to accelerate this transformation. However, questions remain about ChatGPT’s readiness for this challenge, including the techniques and best practices required for success and the scenarios where this approach can consistently deliver results. To explore these questions, we designed a study where a group of master’s students performed programming assignments using TDD, first independently and then with the support of ChatGPT. The three assignments represent distinct scenarios: mathematical calculations (function), text processing (class), and system integration (class with dependencies). We performed a qualitative analysis of the submitted code and reports identifying key strategies that significantly influence success rates, such as providing contextual information, separating instructions in prompts following an iterative process, and assisting AI in fixing errors. Among the scenarios, the integration task achieved the highest performance. This study highlights the potential of leveraging Generative AI in TDD for software development and presents a list of effective strategies to maximize its impact. By applying these positive strategies and avoiding identified pitfalls, this research marks a step toward establishing best practices for integrating Generative AI with TDD in software engineering.

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