On the Understanding of How to Measure the Benefits of Behavior-Driven Development Adoption: Preliminary Literature Results from a Grey Literature Study

Resumo


Behavior-Driven Development (BDD) is the integration of a ubiquitous language with Test-Driven Development and Automated Testing. From this integration, BDD supports software teams to build and deliver software. Although the perceived arguments of better results and of a more efficient development process, we still have no consolidated evidence of such benefits and how to measure them. Therefore, this long-term research aims to characterize how BDD adoption benefits can be measured. To do so, our research design includes a Multivocal Literature Review, composed of a Grey Literature to explore how industry tackles the topic and a Systematic Review to gather scientific evidences, followed of a Snowballing Review to supplement the search. Next, we will conduct empirical studies to characterize the topic from practice. This paper introduces our research and presents the results from our exploratory Grey Literature. We learned from these preliminary results that there are no models or frameworks defined to measure BDD adoption benefits but that teams indeed perceive improvements in software quality, communication, rework rates, among others. We also found that these teams also perceive that team engagement improves with the adoption of BDD and that although there is a certain cost (e.g., time and financial) involved, the investment pays off in the end. These results will inform the design of our Systematic Review and of our downstream empirical studies.
Palavras-chave: Behavior-Driven Development, BDD Adoption, Benefits, Quality Measurement, Grey Literature Review
Publicado
01/12/2020
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COUTO, Thiciane; MARCZAK, Sabrina; GOMES, Fabio. On the Understanding of How to Measure the Benefits of Behavior-Driven Development Adoption: Preliminary Literature Results from a Grey Literature Study. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 19. , 2020, São Luiz do Maranhão. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 373-379.