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Tag: Tabnine

Tricentis Acquires Tabnine to Gain Knowledge Graph for AI Testing Agents

Tricentis today revealed it is acquiring Tabnine to gain access to a knowledge graph that will be used to provide context to AI agents that have been trained to automate a range of testing tasks.

Once the acquisition is complete, Tricentis plans to integrate the knowledge graph developed by Tabnine, dubbed the Enterprise Context Engine, into the company’s Agentic Quality Engineering Platform. Coupled with a vector model, that knowledge graph makes it possible to extract entities, relationships, dependencies, and architectural patterns from repositories, documentation, tickets, application programming interfaces (APIs) and infrastructure metadata in a way that is much easier for AI agents to consume and understand.

The end result will be a platform that continuously ingests code, documentation, tickets, and APIs, providing organizational intelligence in real time and enabling multiple AI agents to share memory and context to both verify outputs and better understand the potential downstream impact of an action.

David Colwell, vice president of AI and machine learning for Tricentis, said that, just as importantly, the capability makes it possible for AI agents to more efficiently test code in a way that serves to reduce the total number of tokens that might otherwise be required.

It’s still early days when it comes to incorporating AI agents into DevOps workflows, but the one thing that is already clear is there is now a greater need to rely on an independent set of AI agents to verify the output of AI coding agents. In some cases, the AI agents performing testing tasks may rely on the same AI model as the coding agents, but as a general rule, the best practice would be to rely on a separate AI model, said Colwell.

Regardless of approach, unless AI agents are integrated into testing workflows, there will be no way for DevOps teams to keep up with the pace at which code is now being generated. The end result would then be more untested code than ever making its way into production environments, which ultimately only serves to increase the total number of incidents that DevOps teams will need to later triage and remediate, noted Colwell.

A recent Tricentis survey finds 60% of organizations already regularly ship untested code into production environments. With more than two-thirds (68%) of organizations having to some degree integrated AI into software delivery workflows, it has only become more probable that untested code is finding its way into production environments. The challenge and the opportunity now is to find a way to reduce that percentage without slowing down the rate at which code is now being created.

There is, of course, a world of difference between shipping code faster and actually delivering more value to the business. The assumption has always been that more code would by definition enable organizations to provide more value to their end customers by delivering more features and capabilities faster. Unfortunately, every time there is an incident it also serves to reduce the return on investment (ROI) from accelerating software delivery in the first place, an issue that in the age of AI has become more concerning as the volume of code being created and deployed exceeds the ability of most DevOps teams to effectively manage.

July 30, 2026
Tabnine Extends Gen AI Platform for Writing Code to Multiple LLMs

Tabnine today revealed that it is giving DevOps teams the ability to switch between multiple large language models (LLMs) when they use its generative artificial intelligence (AI) platform to write code.

Application development teams can continue to use the LLM that Tabnine originally developed for its Tabnine Chat tool for generating code using a natural language interface. Alternatively, they can invoke either an instance of open source Mistral LLM jointly developed with Mistral AI or version 3.5 or 4.0 of the ChatGPT LLM (created by Open AI).

Tabnine president and chief marketing officer Peter Guagenti said the company will continue to add support for other LLMs, given the pace at which multiple LLMs advancements continues to accelerate.

As part of that effort, Tabnine will surface insights into LLM behaviors and their performance, privacy and protection behaviors, so development teams can identify which LLM is best for a specific use case, Guagenti added

Tabnine created an AI agent optimized for software development that optimizes prompts, provides contextual awareness of local and global code bases, and fine tunes LLM output.

There is, of course, no shortage of platforms for generating code, but the LLMs that Tabnine created are trained using a narrower base of code to reduce errors and hallucinations. However, development teams sometimes prefer to use LLMs that can support more parameters. Of course, the more parameters an LLM has, the more expensive it becomes, as the IT infrastructure resource requirement increases.

Regardless of the LLM type, the secret sauce is how they are invoked rather than in the LLMs that are rapidly becoming commodity services, noted Guagenti. Last month, for example, Tabnine made it possible for application development teams to better personalize output by exposing an LLM to both Tabnine’s code base and development environments.

It’s now only a matter of time before generative AI is applied across the entire software development life cycle (SDLC). In the short term, though, the pace at which code is being written should accelerate. The quality of that code will vary depending on the ways those LLMs were trained. A general-purpose LLM such as ChatGPT typically was trained using code examples of varying quality collected from across the Web. As such, that LLM is more likely to convincingly present code that doesn’t actually work, might run inefficiently, or contain vulnerabilities that could be exploited by cybercriminals.

The expectation is the output from LLMs that were been trained using vetted code will provide more reliable results. The challenge and the opportunity then becomes determining how best to apply AI to DevOps workflows before those teams are overwhelmed by the volume of code being generated by machines.

It may still be a while before AI is pervasively applied across the entire SDLC but the genie is out of the proverbial bottle. Eventually the overall code quality that makes it into production environments will improve steadily. In the meantime, DevOps teams should put guardrails in place to make sure the code AI is generating in the short term actually meets their requirements.

April 2, 2024
Tabnine Extends Generative AI Testing Platform by Embracing RAG

Tabnine today revealed that its namesake generative artificial intelligence (AI) platform for creating test code can now surface more accurate and personalized recommendations based on specific code and engineering patterns.

At the same time, Tabnine also announced that Tabnine Chat, a tool for interacting with the large language models (LLMs) at the core of the Tabine platform using a natural language interface, is now generally available.

Data that resides in a codebase or an integrated developer environment (IDE) can now be used to extend the large language models (LLMs) on which the Tabnine platform is based using retrieval augmented generation (RAG) techniques. Previously, the Tabnine platform would only generate test code based on the data that Tabnine had exposed to its LLMs.

Tabnine president Peter Guagenti said RAG makes it possible to provide additional contextual awareness for the test code generated along with documentation and explanations of the test code created. The overall goal is to leverage generative AI to make it easier to shift more responsibility for application testing further left toward developers, he noted.

That approach enables developers to create and run more routine tests earlier in the software development life cycle, which should provide application testing teams with more time to run more complex tests before applications are deployed, added Guagenti. Rather than being a job killer, generative AI will eliminate rote tasks that most members of the DevOps team don’t really enjoy doing, he noted.

Tabnine uses a mix of LLMs it developed with Google to apply generative AI to application testing. Unlike general-purpose LLMs, the ones used by Tabnine were trained using a narrower base of testing code that the company curated using tests made freely available on the internet.

It’s now only a matter of time before generative AI is applied across the entire software development life cycle (SDLC). It’s not clear how automated testing will become in the age of AI, but the overall quality of applications should steadily improve as more tests are run. The goal is to make it possible to conduct more tests without slowing down the overall pace of application development.

There continues to be as much irrational exuberance about AI as fear and loathing. Still, DevOps teams that committed early on to ruthless automation will naturally be at the forefront of adoption. The immediate challenge is assessing what functions will be automated and the impact those changes will have on the way DevOps teams are currently structured. Ultimately, the goal is to eliminate as many of the bottlenecks that conspire to slow application development and deployment while simultaneously improving the quality of the application experience.

It may be a while before generative AI is pervasively applied across DevOps workflows, but one of the first places it will undoubtedly manifest is in test automation. The challenge and the opportunity now is determining how best to apply it in a way that augments DevOps teams that arguably have never had enough time to properly test code in the first place.

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February 22, 2024
Tabnine Unveils Beta of Generative AI Automated Testing Tool

Tabnine this week made available in beta a tool that enables developers to use natural language to interact with the artificial intelligence (AI) models embedded within its test automation platform.

Brandon Jung, vice president of ecosystems for Tabnine, said Tabnine Chat extends generative AI capabilities the company developed to make it simpler for developers to invoke its test automation platform from within their integrated development environment (IDE).

In addition to making it possible to search a codebase more efficiently, that capability can also be used to explain how a piece of code functions, said Jung.

Developers can also generate new code via a natural language interface using Tabnine Chat in the same way other generative AI tools do, he added.

In addition to developing its own LLMs, Tabnine previously announced it will be integrating LLMs provided by Google as part of a strategy to mix and match LLMs regardless of where they are hosted.

Unlike general-purpose LLMs, the ones used by Tabnine were trained using a narrower base of testing code that the company curated using tests made freely available on the internet. No customer data was used to create any of those tests, so developers do not need to worry their code may inadvertently be accessed by anyone else, noted Jung.

In general, as AI makes test creation simpler, tests will be run more frequently, said Jung. Over time, those AI capabilities and more frequent tests should significantly improve the overall quality of applications running in production environments, he added.

It’s now only a matter of time before generative AI is applied across the entire software development life cycle (SDLC). Testing, however, may be one area where it will have the earliest impact.

In the meantime, the debate over the impact generative AI will have on DevOps continues. Some proponents contend most of the software engineering process is about to become automated, while others suggest there will always be a need for a human to be in the loop of any process automated using AI models. Much of the current level of toil associated with DevOps today will be reduced, but the need for software engineers to manage the overall process will remain, they contend.

The one thing that is certain is that the pace at which applications are built and deployed is about to be greatly accelerated. Prior to rise of generative AI, testing processes were barely keeping pace, so as more code is created with the help of AI, the need for similar advances in testing will become acute.

There is as much irrational exuberance about AI as there is fear and loathing, but DevOps teams that committed early on to ruthless automation will naturally be at the forefront of adoption. The immediate challenge is assessing what functions will be automated and the impact those changes will have on the way DevOps teams are currently structured. Ultimately, the goal is to eliminate as many of the bottlenecks that conspire to slow application development and deployment while simultaneously improving the quality of the application experience.

June 30, 2023
Tabnine Extends Generative AI Alliance With Google

Tabnine has extended its alliance with Google Cloud to advance the adoption of generative artificial intelligence (AI) to automate the writing and testing of code.

The generative AI platform provider has already developed its own large language model that is hosted on Google Cloud. Tabnine is now also committing to leveraging the large language model that Google is developing to automate software development life cycle processes.

Brandon Jung, vice president of ecosystems for Tabnine, said it’s apparent that application development and deployment teams will be making use of multiple large language models across SDLC workflows. Not all those large language models will be developed by one single platform provider, he noted.

Rather than being dogmatic about large language models, Tabnine plans to make it possible to invoke multiple models via application programming interfaces (APIs) that are integrated with development environments, he added.

The Tabnine platform supports multiple programming languages, including Python, Java and JavaScript and is designed to be integrated with integrated development environments (IDEs) such as Visual Studio Code and Jetbrains. The company also previously integrated its code completion tool with the GitLab continuous integration/continuous delivery (CI/CD) platform. The overall goal is to make it easier for developers to automatically write code based on custom models using approved source code hosted in a secure private repository.

Generative AI creates a large language model that assesses the probability of what the next line of code will be based on what has preceded it. It’s not likely DevOps teams will be replaced as generative AI is extended across workflows, but the overall size of those teams might shrink as it becomes possible to do more with fewer people. At the same time, the barrier to DevOps adoption will also fall as AI platforms make it simpler for more organizations to embrace DevOps best practices.

The challenge will be making sure the data collected to train AI models is of a high enough quality to ensure the desired outcome. Organizations will need to find a way to apply generative AI to data models that have been validated by DevOps teams.

One way or another, it’s only a matter of time before generative AI capabilities are applied more broadly. Platforms such as OpenAI’s ChatGPT are only the tip of an iceberg that impacts almost every manual process, including software development and deployment. The issue will be determining how quickly those innovations will become practical enough to employ.

In the meantime, it is already apparent generative AI platforms are having a significant impact on the rate at which code can be developed. Inevitably, that means the amount of code moving through DevOps pipelines at any one time should increase significantly. DevOps teams should expect generative AI technologies to be applied both before and after application code is built and deployed, noted Jung.

In the meantime, the biggest issue is, arguably, keeping up with a rate of generative AI innovation that is occurring faster than many organizations can absorb.

March 24, 2023
Tabnine Extends Generative AI Platform Reach to Testing

Tabnine today announced it extended its generative artificial intelligence (AI) platform that enables developers to write code faster to now include the ability to automatically generate tests for that code.

Available in beta, this capability promises to make it easier for developers to test code at the time it is being developed. That approach should make it simpler for developers to address issues without losing any context.

Brandon Jung, vice president of ecosystems and business development at Tabnine, said this latest addition to the Tabnine platform is part of a larger effort to extend the reach of the Tabnine generative AI platform across the entire software development life cycle. The testing capability is expected to be generally available in the third quarter, and Tabnine expects to also employ generative AI to automate a much wider range of tasks across the software development life cycle within the next 12 months.

The Tabnine platform supports multiple programming languages including Python, Java and JavaScript and is designed to be integrated with integrated development environments (IDEs) such as Visual Studio Code and Jetbrains. The company has also previously integrated its code completion tool with the GitLab continuous integration/continuous delivery (CI/CD) platform. The overall goal is to make it easier for developers to automatically write code based on custom models using approved source code hosted in a secure private repository, said Jung.

Generative AI creates a large language model that assesses the probability of what the next line of code will be based on what has preceded it. Tabnine is now extending the model it created to also generate tests for the code its platform helped a developer write. It’s not clear what impact this ability to automate test creation will have on the need for dedicated testing teams, but it is about to become considerably easier to shift more responsibility for testing further left toward developers.

It’s not likely DevOps teams will be replaced as generative AI is extended across workflows, but the overall size of those teams might shrink, noted Jung. DevOps teams will soon be able to do far more with fewer people, he noted. At the same time, the barrier to DevOps adoption will also fall as AI platforms make it simpler for more organizations to embrace DevOps best practices, added Jung.

The challenge will be making sure the data collected to train AI models is of a high enough quality to ensure the desired outcome, noted Jung.

One way or another, it’s now only a matter of time before generative AI capabilities are applied more broadly. Platforms such as ChatGPT developed by OpenAI are only the tip of the iceberg that impacts almost every manual process, including software development and deployment. The issue will be determining at what rate those innovations will become practical to employ. In the meantime, it is already apparent generative AI platforms are having a significant impact on the rate at which code can be developed. Inevitably, that means the amount of code moving through DevOps pipelines at any one time is about to increase significantly.

February 22, 2023
Tabnine, GitLab Build Custom Apps Faster With AI

Tabnine today revealed it has integrated its code completion tool with the GitLab continuous integration/continuous delivery (CI/CD) platform. Tabnine’s platform employs artificial intelligence (AI) to accelerate application development.

Brandon Jung, vice president of ecosystems and business development at Tabnine, said the goal is to make it easier for developers to automatically write code based on custom models created using AI. Those models would reside in the CI/CD platform provided by GitLab. Developers can now create custom models of their private source code, he noted.

The Tabnine AI assistant can then be trained on a customer’s private code, style and security best practices that have been defined by an internal DevOps team, added Jung. That assistant, accessible from within a developer’s integrated development environment (IDE), is conversant in 35 programming languages.

The code completion tool doesn’t eliminate the need for developers to write code; instead, it completes code once developers start writing it based on the model stored in the CI/CD platform.

Nima Badiey, vice president of global alliances for GitLab, said the overall goal is to increase developer productivity at a time when organizations are launching more application development initiatives than ever. Many of those applications are being constructed using multiple programming languages that each individual developer may not have equally mastered.

In addition, an AI model will, over time, improve the quality of the code being created, noted Badiey. That should make it easier to review code that is more accurate because it was created with the aid of a machine, he added.

In general, GitLab has been moving to incorporate AI within DevOps workflows. The company last year acquired UnReview, a provider of a tool that uses machine learning algorithms to identify which code reviewers to assign to a project based on both the quality of their previous efforts and their current workloads.

It’s not clear to what degree AI will be pervasively embedded within DevOps workflows. However, it’s now just a matter of time before algorithms are more widely employed. Organizations that employ DevOps today devote significant resources to maintaining platforms that could be simpler to manage if more tasks and processes were automated. In theory, more resources could be then devoted to building applications versus maintaining DevOps platforms.

One way or another, more organizations are moving to increase developer productivity. Developers are both hard to find and retain, so organizations have a vested interest in finding ways to increase the rate at which an existing pool of developers can build applications capable of driving a wider range of digital business transformation initiatives. Of course, an increase in the rate at which applications are developed also has significant implications for the rate at which they might need to be deployed.

Not everyone in IT is, of course, entirely comfortable with AI. Nevertheless, most DevOps organizations are highly committed to automating as many processes as possible. The only thing that will be different going forward is determining to what degree those processes will be automated by humans versus a machine.

March 2, 2022