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Dataset of Product Knowledge Acquisition Activities used in Multiprovider Software Evolutions

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NIAID Data Ecosystem2026-05-02 收录
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This dataset contains the results of a qualitative study that explores the product knowledge acquisition activities (PKAAs), performed by a development team during software evolution projects. These projects are performed in multiprovider scenarios and consider two specific settings: Setting 1: The provider (i.e., software company) that developed the current version of the system is now in charge of performing its evolution. However, a team different to the one that developed the current version of the system addresses the evolution project. Setting 2: A provider and development team different to the previous one is in charge of the evolution project. Both settings assumed that the initial version was developed as a greenfield project, with the evolution occurring 2-3 years after deploying the previous version of the product. The study followed a qualitative approach, using interviews with 15 SMEs from Brazil (3) and Chile (12). These companies, a mix of small and medium-sized enterprises, had extensive experience in performing custom projects, and utilized agile approaches to do that (particularly, Scrum or ad-hoc agile strategies). The participants were 19 people who played key roles in multiple projects over the past five years (e.g., product owner, scrum master, or project leader). Ten of them participated in evolution project conducted in setting 1, five of them were involved in evolutions performed in setting 2, and four of them were involved in both settings. The participants were interviewed via videoconference, following a semi-structured interview process. The goal of the interviews was to collect information about: 1) the software artifacts that they use to comprehend the software product to evolve, 2) the practices that they use for that purpose, and 3) the cost-effectiveness of using those practice. This interviewing format allowed for engaging discussions and deeper inquiry into unexpected responses.
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2025-02-03
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