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DX: Developer Intelligence Platform

DX is a developer intelligence platform designed to measure productivity, improve data-driven decisions, and facilitate AI-augmented engineering. It helps organizations track AI-generated code, enhance developer experience, and align engineering with business goals.

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DX is a developer intelligence platform crafted by leading researchers to assist organizations in measuring developer productivity and improving decision-making with data. It is particularly focused on navigating the shift to AI-augmented engineering. DX enables companies to measure and improve software health, R&D productivity, and AI-human collaboration, making it a truly AI-native software development lifecycle (SDLC) tool.

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The platform provides capabilities such as AI measurement, which allows organizations to track AI-generated code by commit, pull request, team, agent, and repository. This feature helps in understanding AI's real impact on productivity. Additionally, DX offers AI enablement by building the context layer that AI agents need to operate safely and precisely.

DX also focuses on enhancing the developer experience by identifying how developers work and where they encounter obstacles. This insight helps reduce friction and boost developer satisfaction. Furthermore, the platform aids in measuring engineering productivity by identifying output, finding bottlenecks, and aligning engineering efforts with business objectives.

The platform is used by some of the world's most innovative engineering organizations, demonstrating its effectiveness in driving productivity and innovation. While specific pricing details are not provided, DX is positioned as a critical tool for companies looking to leverage AI in their software development processes.

  • CategoryAI Data Analysis
  • DeploymentCloud
  • PlatformsWeb

  • Measure developer productivity

  • Improve data-driven decisions

  • Navigate AI-augmented engineering

  • Track AI-generated code

  • Enhance developer experience

  • Identify engineering bottlenecks

  • Align engineering with business goals

  • Build context layer for AI agents

  1. Configure: Set up the platform to track productivity metrics

  2. Use: Analyze AI-generated code and developer workflows

  3. Optimize: Identify and address bottlenecks in engineering processes

  4. Enhance: Improve developer satisfaction and productivity

  • AI Measurement
  • Developer Experience
  • Engineering Productivity
  • AI Enablement

From Atlassian

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