With so much movement in the market for AI development tooling, the challenge to properly evaluate choices continues.
AI-powered coding assistants have significantly outperformed conventional evaluation methods, creating gaps between vendor claims and actual performance. This study presents Quality Assessment for AI Development tools, a comprehensive, vendor-neutral framework evaluating coding assistants across six dimensions.
Quality Assessment for AI Development Tools: A Comprehensive Framework for Evaluating AI Coding Assistants Beyond Vendor Claims, by Buse Erol Esirik and Ebru Gökalp.
Some practical takeaways from the quite academic presentation: Evaluate in six different dimensions (linguistic capability, operational quality, generation ability, interaction quality, trustworthiness and sustainability). Model performance likely varies across dimensions. Take stakeholders into account – different stakeholders will weigh dimensions differently.