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This paper surveys 51 software industry practitioners and interviews 11 of them to understand the impact of generative AI on required software engineering skills and shortcomings in university curricula. The study finds that GenAI increases the importance of skills like prompting and output evaluation, while also reinforcing the value of soft skills and traditional competencies. The authors then provide actionable recommendations for academia to better prepare students for modern software engineering environments.
Software engineers need more than just coding skills in the age of GenAI: prompting, critical thinking, and architecture design are now crucial for bridging the gap between academia and industry.
Although tension between university curricula and industry expectations has existed in some form for decades, the rapid integration of generative AI (GenAI) tools into software development has recently widened the gap between the two domains. To better understand this disconnect, we surveyed 51 industry practitioners (software developers, technical leads, upper management, \etc) and conducted 11 follow-up interviews focused on hiring practices, required job skills, perceived shortcomings in university curricula, and views on how university learning outcomes can be improved. Our results suggest that GenAI creates demand for new skills (\eg prompting and output evaluation), while strengthening the importance of soft-skills (\eg problem solving and critical thinking) and traditional competencies (\eg architecture design and debugging). We synthesize these findings into actionable recommendations for academia (\eg how to incorporate GenAI into curricula and evaluation redesign). Our work offers empirical guidance to help educators prepare students for modern software engineering environments.
