What do professional software developers need to know to succeed in an age of Artificial Intelligence?
🤖 AI Summary
In the AI era, developers face skill atrophy and diminished professional adaptability due to overreliance on generative AI. To address this, we conducted in-depth interviews with 21 cutting-edge AI practitioners and employed qualitative thematic coding alongside workplace empirical analysis. We systematically identified 12 categories of development objectives, 75 granular tasks, and their associated competency requirements. This study introduces—first in the literature—the four-dimensional “AI-Augmented Developer” competency framework (encompassing generative AI application, core software engineering, adjacent engineering domains, and non-engineering literacies) and a six-step task adaptation model. We further propose evidence-based educational interventions to mitigate skill degradation, yielding five key insights and actionable recommendations. These findings provide empirical grounding for university curriculum reform and enterprise AI literacy programs, advancing the systematic mapping and evolution of developer competencies.
Application Category
📝 Abstract
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills&knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills&knowledge to be a successful AI-enhanced developer are organized into four domains (using Generative AI effectively, core software engineering, adjacent engineering, and adjacent non-engineering) deployed at critical junctures throughout a 6-step task workflow. In order to"future proof"developers for this age of AI, on-the-job learning initiatives and computer science degree programs will need to target both"soft"skills and the technical skills&knowledge in all four domains to reskill, upskill and safeguard against deskilling.
Problem
Research questions and friction points this paper is trying to address.
Identify essential skills for developers in AI era
Analyze AI's impact on workforce disruption and deskilling
Propose learning strategies for future-proofing developers
Innovation
Methods, ideas, or system contributions that make the work stand out.
Summarized 12 work goals with 75 tasks
Organized skills into four key domains
Proposed on-the-job learning for future proofing
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