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Finding and fixing bugs and errors in a large, enterprise-grade application can be like finding a needle in a haystack. And once you do find the issue, figuring out a suitable fix can be equally difficult, sometimes causing other issues to sprout up that require additional changes. No developer enjoys spending their time being a bounty hunter for errors and issues, yet every developer spends a significant amount of time every week doing remediation work.
Tabnine’s Code Fix Agent empowers developers to fix errors and bugs within their code with the click of a button or a simple command (/fix-code). It ingests any error messages coming from the IDE (if there are any), the code and data in your IDE, and information in your global codebase as context to suggest personalized, accurate improvements to your applications that meet your development organization’s intent.
Tabnine’s Code Fix Agent is seamlessly integrated into Tabnine’s user experience in two different form factors.
Within the Tabnine AI Chat, users can invoke the agent by clicking the Fix code button when starting a new conversation or by typing /fix-code within the prompt bar. The Tabnine Code Fix Agent prompts you to select a block of code within your file and then provides personalized suggestions to fix the errors or bugs within the selected code. You can then hit Insert to add Tabnine’s suggestions to your code file.
If you’re already in your coding flow, you can access the Code Fix Agent within the Inline Actions form factor, either by using the Command Palette (accessible by using the ⌘ + I hotkeys on a Mac) to navigate to the Quick commands dropdown, or by typing /fix-code within the prompt bar. You can also use Codelens at the top of each block of code to generate a personalized suggestion to solve the issues within your code. The Code Fix Agent returns suggestions to fix the selected code in your file shown through a simple diff view, allowing you to clearly see the suggested changes that you can accept, reject, or refine.
For generative AI to truly be impactful, context is everything. Without it, large language models (LLMs) tend to produce responses that, while accurate, are often generic and less aligned with your engineering team’s unique practices. With the right context, however, an AI code assistant can tailor recommendations to fit your team’s specific coding patterns, improving reuse and alignment.
Tabnine achieves this by using locally available code and data within your IDE as context. This includes variable types near the completion point, comments you’ve written, files you’ve interacted with, imported libraries, active projects, and more. By tapping into these sources, Tabnine delivers more relevant and personalized suggestions. Tabnine Enterprise administrators can connect Tabnine to their organization’s code repositories, further expanding the contextual information available. This drastically enhances Tabnine’s ability to suggest accurate fixes and code suggestions that align with your team’s workflow and standards.
The benefits the Tabnine Code Fix Agent provides are vital to a development organization of any size. Developers will spend less time working through bug and error issues, allowing them to do what they do best: innovate. And organizations will ultimately move faster, developing more innovative products much faster due to engineering resources being more effectively used.
The Code Fix Agent is available for all Chat users and is compatible with all IDEs that support Tabnine Chat. Check out our Docs to learn more.
In addition to the Code Fix Agent, Tabnine offers numerous AI agents to accelerate your software development life cycle, including the Code Review Agent, Jira Implementation and Validation Agents, Testing Agent, Code Explain and Onboarding Agent, and Documentation Agent.
If you’re not yet a customer, you can sign up for a free trial of Tabnine. Want to learn about all the plans Tabnine offers? Visit our Plans & Pricing page.