Generative AI Adoption in the Bangladeshi Software Engineering Workforce: A Simulation-Based Analysis of Diffusion, Productivity, and Skills Gaps

The first integrated quantitative analysis of generative AI (GenAI) adoption in the Bangladeshi software engineering workforce for 2022 to 2026. The analysis combines a Bass diffusion model, a reproducible synthetic workforce simulation (N = 1,500) calibrated to BASIS, BBS and World Bank statistics, heterogeneous treatment-effect estimation by English-proficiency stratum, and a skills-gap index by university tier. Four policy scenarios are projected to 2031. The repository contains the full Python pipeline, 40 automated unit tests, 12 publication-quality figures, 11 data tables, and the complete LaTeX manuscript prepared for peer review. Reproducibility: all outputs are deterministic with random seed SEED = 20260524. Licences: source code under MIT; paper, figures, and data tables under CC BY 4.0.