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dc.contributor.authorLin, Junen_US
dc.contributor.authorYu, Hanen_US
dc.contributor.authorPan, Zhengxiangen_US
dc.contributor.authorShen, Zhiqien_US
dc.contributor.authorCui, Lizhenen_US
dc.identifier.citationLin, 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.
dc.description.abstractPurpose: 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.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofInternational Journal of Crowd Scienceen_US
dc.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
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleTowards data-driven software engineering skills assessmenten_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.researchJoint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)en_US
dc.contributor.researchAlibaba-NTU Singapore Joint Research Instituteen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsCrowd-Sourced Design and Engineeringen_US
dc.subject.keywordsTask-Oriented Crowdsourcingen_US
dc.description.acknowledgementThis research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its IDM Futures Funding Initiative; Interdisciplinary Graduate School, NTU; and the Lee Kuan Yew Post-Doctoral Fellowship Grant.en_US
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