AI-Assisted Development in Libyan Enterprises: Impact on SDLC Velocity and Code Quality
TL;DR
It is suggested that AI tool benefits are scale-dependent and require tailored governance frameworks for resource-constrained enterprises and infrastructure constraints and digital literacy gaps moderated tool effectiveness in the Libyan context.
Abstract
The integration of artificial intelligence (AI) tools into software engineering has fundamentally transformed development workflows, yet empirical evidence from developing economies remains scarce. This study investigates the impact of AI-assisted tools on software development lifecycle (SDLC) acceleration and code quality within Libyan enterprise environments. A comparative empirical study involved 48 professional developers across eight enterprises in Tripoli, Misurata, and Benghazi. Participants were stratified by experience level (junior, mid-level, senior) and assigned to either traditional manual development (TMD) or AI-assisted development (AIAD) using GitHub Copilot, ChatGPT, and Amazon Code Whisperer. Metrics included SDLC phase duration, code quality index, defect density, and perceived developer productivity. Results demonstrate that AI tools reduced development time by 22–38% across project scales, with junior developers realizing the greatest gains. However, AIAD projects exhibited a 12–21% increase in code churn, and security vulnerabilities were 1.21 to 2.10 times more frequent depending on project scale (1.21× for small projects, 1.80× for medium projects, and 2.10× for large projects). In large-scale projects, architectural inconsistencies attenuated benefits. Infrastructure constraints and digital literacy gaps moderated tool effectiveness in the Libyan context. These findings suggest that AI tool benefits are scale-dependent and require tailored governance frameworks for resource-constrained enterprises.