Exploring Impact of Requirements Engineering on Other IT Project Areas – Case Study

Requirements Engineering (RE) is recognized as one of the most important (yet difficult) areas of software engineering that has a significant impact on other areas of IT projects and their final outcomes. Empirical studies investigating this impact are hard to conduct, mainly due to the great effort required. It is thus difficult for both researchers and industry practitioners to make evidence-based evaluations about how decisions about RE practices translate into requirement quality and influence other project areas. We propose an idea of a lightweight approach utilizing widely-used tools to enable such an evaluation without extensive effort. This is illustrated with a pilot study where the data from six industrial projects from a single organization were analyzed and three metrics regarding the requirement quality, rework effort, and testing were used to demonstrate the impact of different RE techniques. We also discuss the factors that are important for enabling the broader adoption of the proposed approach

Assisted Requirements Selection by Clustering

Requirements selection is a decision-making process that enables project managers to focus on the deliverables that add most value to the project outcome. This task is performed to define which features or requirements will be developed in the next release. It is a complex multi-criteria decision process that has been focused by many research works because a balance between business profits and investment is needed. The spectrum of prioritization techniques spans from simple and qualitative to elaborated analytic prioritization approaches that fall into the category of optimization algorithms. This work studies the combination of the qualitative MoSCoW method and cluster analysis for requirements selection. The feasibility of our methodology has been tested on three case studies (with 20, 50 and 100 requirements). In each of them, the requirements have been clustered, then the clustering configurations found have been evaluated using internal validation measures for the compactness, connectivity and separability of the clusters. The experimental results show the validity of clustering strategies for the identification of the core set of requirements for the software product, being the number of categories proposed by MoSCoW a good starting point in requirements prioritization and negotiation