With so much development in the market for AI development tooling, the challenge to properly evaluate choices continu...

With so much development 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.

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