Towards a Semi-Automated Data-Driven Requirements Prioritization Approach for Reducing Stakeholder Participation in SPL Development

Requirements prioritization (RP), part of Requirements engineering (RE), is an essential activity of Software Product-Lines (SPL) paradigm. Similar to standard systems, the identification and prioritization of the user needs are relevant to the software quality and challenging in SPL due to common requirements, increasing dependencies, and diversity of stakeholders involved. As prioritization process might become impractical when the number of derived products grows, recently there has been an exponential growth in the use of Artificial Intelligence (AI) techniques in different areas of RE. The present research aims to propose a semi-automatic multiple-criteria prioritization process for functional and non-functional requirements (FR/NFR) of software projects developed within the SPL paradigm for reducing stakeholder participation.

Investigating Fitness Functions for Search-based Requirements Prioritization

It can be challenging for people to select the most relevant requirementamong several software system development options.Requirements prioritization defines the ordering for executing requirementsbased on their priority or importance concerning stakeholders’viewpoints, which is a problematic task. Based on thisproblem, this study aims to present a requirements prioritizationapproach using a genetic algorithm to find optimal solutions, andit can assist in the requirements prioritization activity during thesoftware development process. In this paper, we investigated aset of criteria to create four functions GUT-D, ThS-D, ST, and LT,to assess candidate solutions, i.e., the recommended prioritizedrequirements. We examine the empirical results concerning thepractical approach’s effectiveness and cost computational two experimentsin the evaluation. We found that the 𝐺𝑈𝑇 − 𝐷 fitnessfunction achieved the best fitness value in different settings with90.51% and 98.63%. Besides that, our study demonstrates that the approachis promising to assist requirements prioritization since eachfitness function can be used individually according to companies’necessities.