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Aligning software engineering education with industrial needs: A meta-analysis
Wageningen University and Research Centre, NLD.
Independent Researcher, TUR.
Bilkent Üniversitesi, TUR.
Wageningen University and Research Centre, NLD.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 156, s. 65-83Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Context: According to various reports, many software engineering (SE) graduates often face difficulties when beginning their careers, which is mainly due to misalignment of the skills learned in university education with what is needed in the software industry. Objective: Our objective is to perform a meta-analysis to aggregate the results of the studies published in this area to provide a consolidated view on how to align SE education with industry needs, to identify the most important skills and also existing knowledge gaps. Method: To synthesize the body of knowledge, we performed a systematic literature review (SLR), in which we systematically selected a pool of 35 studies and then conducted a meta-analysis using data extracted from those studies. Results: Via a meta-analysis and using data from 13 countries and over 4,000 data points, highlights of the SLR include: (1) software requirements, design, and testing are the most important skills; and (2) the greatest knowledge gaps are in configuration management, SE models and methods, SE process, design (and architecture), as well as in testing. Conclusion: This paper provides implications for both educators and hiring managers by listing the most important SE skills and the knowledge gaps in the industry. © 2019 Elsevier Inc.

sted, utgiver, år, opplag, sider
Elsevier Inc. , 2019. Vol. 156, s. 65-83
Emneord [en]
Important skills, Industry needs, Knowledge gap, Meta-analysis, Software engineering education, Systematic literature review (SLR), Engineering education, Professional aspects, Software engineering, Software testing, Well testing, Knowledge gaps, Meta analysis, Information management
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-18210DOI: 10.1016/j.jss.2019.06.044Scopus ID: 2-s2.0-85067367468OAI: oai:DiVA.org:bth-18210DiVA, id: diva2:1331628
Tilgjengelig fra: 2019-06-27 Laget: 2019-06-27 Sist oppdatert: 2019-06-27bibliografisk kontrollert

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