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      • Department of Computer Engineering
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      Aligning software engineering education with industrial needs: a meta-analysis

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      Embargo Lift Date: 2021-10-01
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      Author(s)
      Garousi, V.
      Giray, G.
      Tüzün, Eray
      Catal, C.
      Felderer, M.
      Date
      2019
      Source Title
      Journal of Systems and Software
      Print ISSN
      0164-1212
      Publisher
      Elsevier
      Volume
      156
      Pages
      65 - 83
      Language
      English
      Type
      Article
      Item Usage Stats
      143
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      143
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      Abstract
      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
      Keywords
      Software engineering education
      Industry needs
      Important skills
      Knowledge gap
      Systematic literature review (SLR)
      Meta-analysis
      Permalink
      http://hdl.handle.net/11693/53069
      Published Version (Please cite this version)
      https://dx.doi.org/10.1016/j.jss.2019.06.044
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