Tabnine announced an expanded partnership and integration within the Atlassian platform to build and maintain applications fast and with high quality through AI. Unveiled at the Atlassian Team ?24 conference, the collaboration company is making it easy than ever to integrate AI-enabled software development tools directly with Atlassian?s suite of products. Atlassian software partners will be able to leverage Atlassian Rovo and the company?s Teamwork Graph to extend and enhance their AI agents.

Once available, these new integrations will allow Tabnine?s customers to leverage the complete body of information ? the data and code that represents deep institutional knowledge ? that?s captured across Atlassian Jira, Confluence, and BitBucket.

Additionally, Tabnine?s first-to-market AI coding assistant will be embedded as an agent within the Atlassian product suite to provide the company?s full scope of generative AI features anywhere an engineering team does their work. These new capabilities are expected to be available in Atlassian?s products later this year. Tabnine is an Atlassian Ventures-backed company and a long-time Atlassian customer, with significant integrations already available.

Atlassian customers can already benefit from the partnership with Tabnine, including: Tabnine is integrated with Atlassian Bitbucket through the developers IDE to provide critical context to personalize the behavior of the AI coding assistant and ensure that code generated by Tabnine is an ideal fit for each engineering team. Personalized AI recommendations based on awareness of a developer?s code are accepted 40% more often than AI recommendations generated without these integrations. Tabnine is further integrated with Atlassian Bitbucket to even more explicitly guide recommendations and to leverage a company?s committed codebases as a north star for the AI coding assistant to optimize recommendations towards. Tabnine administrators can connect Tabnine to their organization?s Bitbucket repositories (alongside any other Git-based tools, including GitHub and GitLab) to deliver more personalized, higher quality results when generating code, explaining code, creating tests, writing documentation, and more.

In addition, this connection allows a developer to use plain language queries to better understand an existing codebase, to find code that serves specific functions or leverages specific APIs, and to identify code that can be reused on their project. This capability is currently in Private Preview for Tabnine Enterprise customers. Tabnine can use a customer?s code base stored in Bitbucket to fine tune Tabnine?s custom-trained software development AI model.

Leveraging a company?s codebase to extend Tabnine?s existing models results in higher performance in common software development tasks and can dramatically improve the quality of code generated for companies working in less common programming languages or frameworks.