Pre-AI Baseline: Developer IDE Satisfaction and Tool Autonomy in 2022

TL;DR

This study provides a baseline of developer IDE satisfaction and tool autonomy before AI adoption, highlighting high satisfaction linked to autonomy and low cloud IDE usage, serving as a reference for future AI impact assessments.

Contribution

It offers the first comprehensive pre-AI baseline of developer IDE satisfaction and tool autonomy, enabling future comparisons of AI's impact on software development workflows.

Findings

High overall IDE satisfaction (mean=8.14)

Tool autonomy strongly predicts satisfaction (beta=0.51)

Low cloud IDE adoption (4.3%) due to network issues

Abstract

To quantify the impact of AI on software development, the community requires a robust pre-AI baseline. This study analyzes valid satisfaction data from 1,155 software developers collected in July 2022, immediately preceding the mainstream adoption of generative AI tools. We report a high-satisfaction ecosystem (Mean = 8.14 [95% CI 8.01-8.25]), dominated by Visual Studio Code (79% usage). Multivariable regression confirms that autonomy in tool choice is the strongest predictor of IDE satisfaction (beta = 0.51), significantly outweighing demographic or role-based factors. Conversely, cloud IDE adoption was negligible (4.3% regular usage), with 40.1% citing network dependency as the primary barrier, a constraint that remains relevant for modern cloud-reliant AI agents. Additionally, we identify an "experimenter" segment (29.9%) characterized by high tool churn but no significant satisfaction difference (t = 0.43, p = 0.67), and demonstrate significant variation in IDE retention rates (VS Code: 68.5%, traditional IDEs: 3.9-25%), suggesting underlying dissatisfaction despite high overall satisfaction. By providing a quantitative snapshot of developer sentiment and workflows on the eve of the AI revolution, this study establishes a verifiable baseline for longitudinal research into the productivity-satisfaction misalignment observed in the post-AI era.