Software Engineering Teamwork Data Understanding using an Embedded Feature Selection

doi:10.23940/ijpe.21.05.p6.464472

Abstract

Abstract: Teamwork plays an essential role in determining the outcome of software engineering projects, especially when software is being developed by large teams in geographically distributed environments. To understand the successful development of these types of projects, it is important to assess the required teamwork skills that would help in resolving possible problems and avoiding failure. However, it is still not clear how to assess teamwork skills. In this paper, we propose an analytical framework based on a machine learning algorithm to study teamwork skills and factors that influence the success/failure of software engineering projects. For this purpose, we conduct our study on the Software Engineering Teamwork Assessment and Prediction (SETAP) dataset using a machine learning algorithm to extract the relevant features. The dataset provides quantitative data of team activity measures related to the software engineering process and the product at the different software development lifecycle phases. The results show that each of the software lifecycle phases requires different teamwork skills. The results demonstrate the efficiency of the approach; that has predicted team outcomes by accuracy score greater than 90% for process and product data.

Key words: software engineering, teamwork, machine learning, features selection, gradient boosting

Mohamed Amine Beghoura. Software Engineering Teamwork Data Understanding using an Embedded Feature Selection [J]. Int J Performability Eng, 2021, 17(5): 464-472.

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