The new Crowdbotics extension for GitHub Copilot takes advantage of all the requirements and context in the Crowdbotics platform to help developers generate more accurate code with Copilot. Integrated with GitHub Copilot Chat, the extension enables developers to benefit from this accuracy improvement without ever having to leave their development environment.

A recent joint research study conducted by Crowdbotics, GitHub, and Microsoft using a subset of the Crowdbotics extension features, found that injecting business requirements from Crowdbotics PRD AI into GitHub Copilot’s neighboring tab context model, improved GitHub Copilot code suggestion acceptance rate by 14%. This change reflects a 51% relative improvement in the acceptance rate. Additionally, the study found that developers using this multi-model configuration were 25% more likely to succeed at feature development than non-AI assisted developers. The now-publicly available Crowdbotics extension has this feature built in, along with a number of other additional features to help developers stay “in flow” longer.

“The Crowdbotics extension for GitHub Copilot achieves what both GitHub and Crowdbotics aim to do: improve developers’ lives by making their code smarter and more accurate,” said Anand Kulkarni, CEO at Crowdbotics. “Product requirements are the holy grail when it comes to making coding more efficient, so harnessing the power of this extension is a no-brainer for any developer looking to speed up their workflows without compromising context or accuracy.”

Benefits of the Crowdbotics extension include:

• Break features into decomposed layers, such as front end, back end, business logic, data schema or third-party integrations.

• Technical recommendations for the integrations best suited for the app and development team.

• Seamless connections between developers and PRDs without disrupting workflows or needing to switch to different windows.

The Crowdbotics extension is available now, with a free 30-day trial available through GitHub Marketplace.

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With Testim Copilot, users can input a text description of what they want to test and receive the JavaScript code for that test. In addition to providing test code, it also provides an explanation of that code, which makes it easier to understand and potentially reuse that code for future tests.

By incorporating generative AI into the testing process, Tricentis hopes to make testing accessible to testers with not as much technical knowledge. For users of all experience levels it has the potential to save time and reduce errors.

According to Tricentis, customers who have already been using its AI tools have lowered their test failure rate by 16%. 

“Testim Copilot puts AI directly into the hands of the user, automatically suggesting test cases and fixes, meaning more time spent on workflows to boost productivity and improve time to market for new applications,” said Mav Turner, chief product and strategy officer at Tricentis. “This is only the beginning – we expect future Tricentis Copilot releases to have even greater benefits.”

The next Tricentis products to get AI Copilots will be Tricentis Tosca (currently in beta) and Tricentis qTest. All of the Tricentis Copilots will be available as add-ons to each of the products. 

The company will also be hosting a webinar on May 7 to talk about its AI strategy and its upcoming AI Copilot releases. 

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It utilizes Appian’s enterprise AI architecture and private AI strategy to deliver comprehensive process automation solutions swiftly.

In its first version, AI Copilot employs generative AI to transform PDFs and structured forms into user-friendly digital interfaces, enhancing user experiences. This tool streamlines the conversion of PDF forms into interactive business apps, enabling developers to use low-code design tools for fine-tuning. 

Future AI Copilot features will expand its generative AI functions to build workflows using natural language and to access Appian’s data fabric architecture for self-service analytics and immediate report generation.

“The AI copilot will augment and accelerate the end-to-end application development capabilities of the Appian Platform,” said Chandra Surbhat, the experience practice head and VP for Enterprise Futuring at Wipro. “Leveraging generative AI will simplify the software development on an already low-code platform. Plus, clients will use Appian’s private AI strategy, which addresses their critical privacy and security concerns. We look forward to tapping into Appian AI Copilot and unlocking greater business value for our customers.”

There are also additional new capabilities in the latest release of the Appian Platform. One is a more secure and scalable data fabric. Data fabric scalability has doubled to 4 million synced rows per record so users can unify their enterprise data to power and train AI services with built-in guardrails that protect the security and privacy of data, the company explained.

The tool offers an improved AI Skill Designer experience that is user-friendly. Creating custom and private AI models at scale is simplified, including the ability to test models before deploying them. Users can train and modify models for tasks like document and email classification, and seamlessly integrate them into workflows similar to other design elements.

Lastly, the new release has a more unified Appian RPA to create, manage, and deploy robotic tasks directly from Appian Designer using the familiar and intuitive design object experience. With the addition of groups and role maps, users can efficiently manage access and permissions for robotic tasks, enhancing security and control.

Additional details are available here. 

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