2021
DOI: 10.1145/3464431
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Identifying Non-Technical Skill Gaps in Software Engineering Education: What Experts Expect But Students Don’t Learn

Abstract: As the importance of non-technical skills in the software engineering industry increases, the skill sets of graduates match less and less with industry expectations. A growing body of research exists that attempts to identify this skill gap. However, only few so far explicitly compare opinions of the industry with what is currently being taught in academia. By aggregating data from three previous works, we identify the three biggest non-technical skill gaps between industry and academia for the field of softwa… Show more

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Cited by 10 publications
(4 citation statements)
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“…As the history and evolution of software engineering has shown, both the number of dimensions and the nature of each dimension are a function of time, and can therefore change over time. In particular, there is ever increasing attention on non-technical dimensions, especially soft skills [34]. SEE needs to evolve accordingly [35].…”
Section: Am No Longer Accepting the Things I Cannot Change I Am Chang...mentioning
confidence: 99%
“…As the history and evolution of software engineering has shown, both the number of dimensions and the nature of each dimension are a function of time, and can therefore change over time. In particular, there is ever increasing attention on non-technical dimensions, especially soft skills [34]. SEE needs to evolve accordingly [35].…”
Section: Am No Longer Accepting the Things I Cannot Change I Am Chang...mentioning
confidence: 99%
“…Additionally, this could help to the address 'gaps' highlighted in recent work (e.g. [617]) between what students learn and the skills that are expected and needed in the private sector.…”
Section: What Needs To Be Done?mentioning
confidence: 99%
“…Research has also found that, in companies, the seniority level showed little correlation to actual programming skill [39], and programmers with similar education and background can exhibit vast differences in productivity, up to factors of ten [49]. Research is still at a loss when it comes to explaining the cause of these differences, accounting for different trajectories of learning that underlie programming education and training [24,32], or identifying proficient programmers during a hiring process [46].…”
Section: Introductionmentioning
confidence: 99%