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|Title:||Towards data-driven software engineering skills assessment||Authors:||Lin, Jun
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2018||Source:||Lin, J., Yu, H., Pan, Z., Shen, Z. & Cui, L. (2018). Towards data-driven software engineering skills assessment. International Journal of Crowd Science, 2(2), 123-135. https://dx.doi.org/10.1108/IJCS-07-2018-0014||Journal:||International Journal of Crowd Science||Abstract:||Purpose: Today’s software engineers often work in teams to develop complex software systems. Therefore, successful software engineering in practice require team members to possess not only sound programming skills such as analysis, design, coding and testing but also soft skills such as communication, collaboration and self-management. However, existing examination-based assessments are often inadequate for quantifying students’ soft skill development. The purpose of this paper is to explore alternative ways for assessing software engineering students’ skills through a data-driven approach. Design/methodology/approach: In this paper, the exploratory data analysis approach is adopted. Leveraging the proposed online agile project management tool – Human-centred Agile Software Engineering (HASE), a study was conducted involving 21 Scrum teams consisting of over 100 undergraduate software engineering students in multi-week coursework projects in 2014. Findings: During this study, students performed close to 170,000 software engineering activities logged by HASE. By analysing the collected activity trajectory data set, the authors demonstrate the potential for this new research direction to enable software engineering educators to have a quantifiable way of understanding their students’ skill development, and take a proactive approach in helping them improve their programming and soft skills. Originality/value: To the best of the authors’ knowledge, there has yet to be published previous studies using software engineering activity data to assess software engineers’ skills.||URI:||https://hdl.handle.net/10356/162313||ISSN:||2398-7294||DOI:||10.1108/IJCS-07-2018-0014||Rights:||© Jun Lin, Han Yu, Zhengxiang Pan, Zhiqi Shen and Lizhen Cui. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||IGS Journal Articles|
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