Software Engineer Competency Framework in the Era of Generative AI: A Literature Review
TL;DR
This study identifies a critical research gap: competency evolution at the senior engineering tier remains substantially under-researched compared to junior and mid-level stages and offers practical implications for software organizations and educational institutions in redesigning competency development pathways in the GenAI era.
Abstract
Generative artificial intelligence (GenAI) technologies such as Claude Code, ChatGPT, and GitHub Copilot are fundamentally reshaping software development practices, shifting the core activities of software engineers from direct code authoring toward validation, orchestration, and architectural reasoning. This paradigm shift raises fundamental questions: what competencies do software engineers require to collaborate effectively alongside GenAI, and how do these requirements vary across career stages? A focused literature review informed by the Systematic Literature Review principles of Kitchenham & Charters (2007) and the mapping study guidelines of Petersen et al. (2015) was conducted to address these questions. Findings were synthesized into a three-pillar competency model: foundational technical competencies augmented by AI tool literacy and prompt engineering; cognitive-analytical skills characterized by intensified critical code review, systems thinking, and AI-generated logic verification; and meta-skills encompassing AI governance, ethical judgment, and continuous adaptability, all of which exhibit significant differentiation across junior, mid-level, and senior software engineers. Furthermore, this study identifies a critical research gap: competency evolution at the senior engineering tier remains substantially under-researched compared to junior and mid-level stages. These findings offer practical implications for software organizations and educational institutions in redesigning competency development pathways in the GenAI era