Towards data-driven software engineering skills assessment. Issue 2 (11th June 2018)

• Record Type: Journal Article
• Authors: Lin, Jun; Yu, Han; Pan, Zhengxiang; Shen, Zhiqi; Cui, Lizhen
• Is Part Of: International journal of crowd science. Volume 2:Issue 2(2018)
• Page Start: 123
• Page End: 135
• Publication Date: 2018-06-11
• Subjects: Crowd-sourced design and engineering -- Task-oriented crowdsourcing -- Agile software engineering -- Tools and platforms to support crowd science and engineering; Human-computer interaction -- Periodicals; Human computation -- Periodicals; Cooperating objects (Computer systems) -- Periodicals; 621.3984
• DOI: 10.1108/IJCS-07-2018-0014
• Languages: English
• ISSNs: 2398-7294

Abstract

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.