The popularity of generative AI has broken out of tech circles and become a household term, with seemingly everybody talking about the amazing things that AI can do for them.
But for many software developers, Generative AI has been an integral part of their lives for quite some time. Code assistants such as Tabnine and others have reached wide adoption among developers, and we’re now seeing more and more demand for an AI development assistant at the organization level, and not just at the individual developer level.
In the following sections, we’re going to describe Tabnine Enterprise and how it helps organizations create higher quality code faster with the help of AI.
Why do enterprises need an AI Development Assistant system?
Organizations can expect to gain significant benefits by deploying AI in their SDLC:
- Ship software faster
The number one thing that engineering teams look for when considering AI solutions is to do more with less, and no wonder – AI can now dramatically accelerate development by automating larger and larger portions of repetitive coding and improving speed, while also preventing errors by helping developers get on the “right track”.
As of today, Tabnine generates about 30% of our users’ code. Using AI in the SDLC can also significantly speed up code review and reduce errors by automating certain tasks. By generating code using natural language inputs, it can also help developers express their intent more clearly. Additionally, AI-generated code can be consistent in terms of style, formatting, and naming conventions. This makes it easier for reviewers to understand and identify potential issues. Furthermore, AI-generated code can be analyzed and evaluated for errors before it is even written, which can help catch mistakes early on in the development process. AI can also help with code refactoring and optimization by identifying patterns and patterns of inefficiency in the codebase, which can help improve the overall performance and maintainability of the code. 2. Onboard new team members faster
AI models that are connected to an enterprise’s code repository are able to generate high-quality, consistent code that’s based on the best practices, naming conventions, styles, and formatting of a specific dev organization. This helps save time and reduce the learning curve for new team members, so they can start coding faster. This also relieves senior developers of the training burden, allowing them to focus on the more complex and valuable parts of the development process - Improve overall code quality and consistency
By generating repetitive code and providing code completions based on an organization’s code repositories, AI code assistants help ensure that code is readable and understandable, promoting code consistency through style, formatting, and naming conventions. This results in higher-quality code, fewer errors, and a codebase that’s easier to maintain, making code review faster and easier. As a result, bugs are caught at an earlier stage, before production or integration. - Improve developer satisfaction and happiness
Developers are constantly looking for the best tools to improve their productivity and efficiency, and AI is one of them. By providing developers with an effective AI code assistant that’s specifically designed for enterprise R&D teams, companies can help ensure that developers have what they need to fulfill their full potential.
There are several important features and capabilities that AI code assistants should offer organizations, including:
- High-quality code suggestions
AI code assistants should offer organizations high-quality code suggestions that are accurate and relevant to the current context. - Quality and consistency
AI code assistants should be able to conform to the organization’s best practices, coding standards, and naming conventions, styles, and more, in order to ensure quality and consistency of code. - Ability to use Intellectual property of code created using AI
Organizations should be able to use the intellectual property of code created using AI, as well as have the ability to customize the AI system to their specific needs, without facing the possibility of legal exposure or risk. - Privacy and security of the system
AI code assistants should offer organizations privacy and security features that prevent code leakage and comply with the company’s security policy and regulations. - Smooth integration
AI code assistants should integrate smoothly with the existing tools and processes currently used by the organization, with minimal disruption to the development workflow. - Reporting and monitoring
AI code assistants should offer clear reporting on how effective the AI system is, including metrics on time saved, errors prevented, and code quality improvements. - Compliance
AI code assistants should be compliant with industry regulations, such as GDPR and HIPAA, to protect user data privacy. - Scalability
AI code assistants should be scalable to accommodate the growth of the organization and adapt to changing needs. - Support
AI code assistants should offer support for a variety of programming languages and frameworks, and provide ongoing updates and maintenance to stay current with new technologies.
What makes Tabnine a great choice for enterprises considering adoption of AI in their SDLC?
Tabnine Enterprise offers contextual code suggestions that automate repetitive coding, generating high-quality, best-practice code. Based on Large Language Models trained on billions of lines of code from credible open source licenses, Tabnine provides:
- Whole-line code completion
- Full function or snippet
- Text to code
Tabnine generates ~30% of code, contributing to the following factors:
- Faster development by keeping developers in the flow and removing the need for search
- Preventing errors by putting developers on the right track
- Expanding developer knowledge
- Shortening code-review iterations
Smooth integration into the existing development workflow
Unlike AI chatbots like ChatGPT, Tabnine perfectly integrates into existing tools and processes. This means that no process change is required and you start getting value from day one. Tabnine functions as an extension of the development workflow directly within IDEs, with plugins available for all recent versions of Visual Studio Code, IntelliJ (and all JetBrains IDEs), Jupyter Labs, Visual Studio (full support for VS 2022 coming Q3), and Eclipse (full support coming Q3). Implementation is both fast and painless!
Battle-tested with millions of developers
Initially released in 2018, Tabnine isn’t only the most mature AI assistant for software development, but with millions of users worldwide, it’s also the most widely used product on the market. This is important because expertise matters. While many companies can train or serve Large Language Models for code prediction, the real trick is serving the day-to-day needs of the developers with the right suggestion at the right time with the correct scope and context. Tabnine is the result of countless iterations and improvements based on feedback from professional developers who use our product every single day.
Trained on code with permissive license only (no GPL etc., no ambiguity)
Tabnine is only trained on open-source code with a permissive license.. This decision has painful implications for Tabnine in terms of acquiring training data, but it helps ensure that developers can use the code that Tabnine generates in commercial projects without uncertainty about open-source licenses. Moreover, training our AI on code with permissive licenses only fully respects the intent of the developers who contributed code to open source.
Learn more about how we keep our users’ complete privacy.
Provides tailored guidance by learning private projects code and patterns
While AI that’s been trained on open-source code can definitely accelerate development, projects of significant size have an “internal language” comprised of internal services, frameworks, and libraries with their APIs and idiomatic patterns of how to accomplish certain tasks in the codebase. Tabnine Enterprise’s AI models provide fully secured, tailored guidance by learning private projects’ code and patterns, making the AI assistance especially relevant when working with internal APIs and patterns. This increases not only the speed of development but also the consistency of the code and the ease of onboarding onto a new codebase.
Enterprise-grade security
Source code is a core asset of companies, and as such, security of services touching code is critical and typically needs to meet certain standards to comply with corporate regulations. Tabnine prioritizes user security, implementing robust measures to keep your data safe:
- Ability to run inside your network: Tabnine can (optionally) run inside your Virtual Private Cloud and even on your own servers, ensuring no code leaves your trusted network
- Tabnine doesn’t train its general AI models on code created by our users
- SOC-2 certified
- Coming soon:single-sign-on with your internal service for authentication and authorization
Future-proof architecture
Tabnine’s architecture decouples the product from any specific AI model used as a basis, while also connecting to any additional foundational models as soon as they become available. This means that when you choose Tabnine, you get on a platform that’s continuously improving, not just thanks to Tabnine’s own innovation, but also thanks to other community efforts for training better and stronger foundational models.
About Tabnine Enterprise
Tabnine Enterprise is an AI code generation tool that helps software engineering teams write high-quality code faster and more efficiently, accelerating the entire SDLC. Designed for use in enterprise software development environments, Tabnine Enterprise offers a range of features and benefits, including the highest security and compliance standards and features, as well as support for a variety of programming languages and IDEs.
