A Case Study on a Hybrid Approach to Assessing the Maturity of Requirements Engineering Practices in Agile Projects (REMMA)

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Abstract

Context: Requirements Engineering (RE) is one of the key processes in software development. With the advent of agile software development methods, new challenges have emerged for traditional, prescriptive maturity models aiming to support the improvement of RE process. One of the main problems is that frequently the guidelines prescribed by agile approaches have to be adapted to a project’s context to provide benefits. Therefore, it might be naive to believe that it is possible to propose a prescriptive method of RE process improvement that will suit all agile projects without any alteration. Objective: The aim of the paper is to evaluate a hybrid approach to assessing the maturity of agile RE (REMMA), which combines elements of prescriptive and problem-oriented improvement methods. Method: The usefulness, ease of use, and cost-effectiveness of REMMA were investigated through a case study performed in one of the biggest software houses in Central Europe. Results: The results of the case study suggest that the method seems easy to use, affordable, and is perceived as a useful tool to support the process of improving RE practices in agile projects. Its feature of taking into account the dependencies between practices and the necessity to adapt them to a certain project context was regarded as well suited for the agile context. Conclusions: REMMA, which includes two main components: a maturity model for agile RE (a set of state-of-the-art agile RE practices) and an assessment method that makes it possible to evaluate how well the agile RE practices are implemented, seems to be a useful tool supporting improvement of RE in agile projects.

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APA

Ochodek, M., Kopczyńska, S., & Nawrocki, J. (2020). A Case Study on a Hybrid Approach to Assessing the Maturity of Requirements Engineering Practices in Agile Projects (REMMA). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12011 LNCS, pp. 689–698). Springer. https://doi.org/10.1007/978-3-030-38919-2_58

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