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Software Quality Engineering Will Be Defined by AI Technology

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The Rise of AI in Software Testing: Tricentis Introduces Tosca Copilot

In the world of software engineering, the integration of artificial intelligence (AI) is revolutionizing the way developers create and manage code. AI is now being applied to automate software testing, clean up code, and enhance productivity across all phases of the testing lifecycle. One company leading the charge in this space is Tricentis, with their newly announced Tricentis Tosca Copilot tool.

Tosca Copilot is a generative AI assistant that helps quality assurance teams optimize test assets through a chat interface. By leveraging advanced large language models and proprietary embeddings, the Copilot promises to streamline testing tasks, increase productivity, and reduce redundancy in software stacks. The tool has been beta-tested by various organizations, with reported benefits including a reduction in test failure rates and an increase in test case generation.

The integration of AI with testing levels the playing field for all software team members, allowing even citizen testers to play a greater role in testing. This democratization of technology access is driving greater productivity, faster time to market, and lower costs. Tricentis has built test portfolio optimization functions into Tosca Copilot, enabling users to quickly identify and modify test cases, duplicates, unlinked assets, and specific executions.

As software systems become more complex, the need for thorough testing becomes paramount. Tricentis emphasizes the importance of testing all code, whether human-generated or machine-generated, to the same regulatory standards. By leveraging a comprehensive set of embeddings and retrieval augmented generation (RAG) AI services, Tricentis ensures that the testing process is thorough and effective.

Tosca Copilot also provides insights into application code execution, helping developers troubleshoot issues and defects faster. By interpreting execution results and providing actionable insights, the tool shortens software application release cycles and increases productivity gains. Beta tester organizations have reported more efficient workflows and cost savings through the elimination of redundant testing tasks.

Overall, the integration of AI into software testing processes is ushering in a new age of workflow efficiency. By automating repetitive tasks and streamlining testing processes, AI is enabling developers to focus on more strategic and creative aspects of their work. This shift towards more efficient workflows is a testament to the power of AI in enhancing software development practices and driving innovation in the industry.

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