Creating benchmarkable components to measure the quality ofAI-enhanced developer tools

2025-04-16

AI Summary

Current generative AI products lack standardized, developer experience (DX)-centric benchmarks, leading to an overemphasis on model performance at the expense of tool practicality and hindering rigorous competitive analysis. Method: This paper introduces the first benchmarkable DX evaluation framework for AI development tools, integrating AI attitude surveys, standardized task engineering, and a dual-dimensional assessment—spanning both functional capabilities and task-level usability. It employs a mixed-methods approach comprising structured questionnaires, controlled task design, UX metrics, statistical analysis, and modular development. Contribution/Results: The resulting open-source, reusable, enterprise-grade benchmark suite significantly lowers the barrier to quantitative DX measurement. It enables systematic, cross-product horizontal evaluation and fills a critical gap in the standardized, holistic assessment of developer experience for generative AI tools.

Technology Category

• Humans and AI: User Experience and Usability
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

In the AI community, benchmarks to evaluate model quality are well established, but an equivalent approach to benchmarking products built upon generative AI models is still missing. This has had two consequences. First, it has made teams focus on model quality over the developer experience, while successful products combine both. Second, product team have struggled to answer questions about their products in relation to their competitors. In this case study, we share: (1) our process to create robust, enterprise-grade and modular components to support the benchmarking of the developer experience (DX) dimensions of our team's AI for code offerings, and (2) the components we have created to do so, including demographics and attitudes towards AI surveys, a benchmarkable task, and task and feature surveys. By doing so, we hope to lower the barrier to the DX benchmarking of genAI-enhanced code products.

Problem

• Lack of benchmarks for genAI-enhanced developer tools
• Focus on model quality over developer experience
• Difficulty comparing products with competitors

Innovation

• Modular components for DX benchmarking
• Surveys on demographics and AI attitudes
• Benchmarkable tasks for code products