Evaluating Software Developers’ Acceptance of a Tool for Supporting Agile Non-Functional Requirement Elicitation

Abstract

Due to the need for flexibility to requirements changes, agile software development methods have been attracting the attention of academic and industrial domains. Unlike traditional approaches, agile methods focus on the rapid delivery of business value to customers through empirical and incremental development processes. Despite being effective in delivering quality functional requirements, agile practices generally neglect non-functional requirements until the later stages of software development. However, neglecting non-functional requirements during requirements analysis can lead to project failures. In this paper, we present the NFRec tool, which aims to support software developers in the elicitation of non-functional requirements in the context of agile software development. Additionally, we report the results from a case study to evaluate the acceptance of the NFRec tool from the point of view of software developers of four projects from a Brazilian software company. To gather information about the tool acceptance, we applied a questionnaire based on the indicators from the Technology Acceptance Model. Overall, the four teams considered the NFRec tool useful and easy to use for supporting the management of non-functional requirements in agile projects.

A Genetic Algorithm-Based Approach to Support Forming Multiple Scrum Project Teams

“…In agile projects, including Scrum, the most popular notation to document business requirements is the User Story [52]. Using an approach such as the ones presented in Dantas et al [5], Ramos et al [6], and Elamin and Osman [15] to add link semantics between US enable inferring the similarity between them; thus, facilitating the reuse of US for new projects. Since most agile tools automatically construct traceability between US and tasks (i.e., a one-to- many relationship), it is possible to reuse tasks having the US as the index.…”

“…For organizations with a high rate of similarity between projects, there is the potential for reusing knowledge from past projects to assist in decision making. For instance, Dantas et al [5] analyzed the similarity between the features of a target project and historical data to estimate effort, and Ramos et al [6] have done the same to infer non-functional requirements. A similar approach is possible to address the MTF problem if the company has detailed information about the work done by each collaborator.…”

“…This information is used to build a detailed profile of the developers. Plus, with the support of a US-reuse framework, such as the ones presented in Dantas et al [5], Ramos et al [6], and Elamin and Osman [15], it enables to predict the tasks to be developed in projects before their execution, enabling to build detailed project profiles. Second, the detailed developers' and projects' profiles are used as input into a Genetic Algorithm, which searches for the best global team formation for Scrum projects.…”