Best Practice

Elevate your software testing with Generative AI

Concept Reply's testing assistant agent blends Generative AI with advanced Quality Assurance processes for efficient, precise software testing automation.


Introducing the testing assistant agent

The testing assistant agent developed by Concept Reply represents a significant leap in software testing, powered by advanced Generative AI. This solution integrates effortlessly with the existing Reply Test Automation Framework, enhancing the Quality Assurance process with its ability to autonomously generate structured test books and assist in developing automated tests.

Designed for efficiency and precision, the agent optimises the testing lifecycle, addressing complex requirements with tailored automation. It embodies the synergy of AI-driven innovation and meticulous testing standards, applying them in different scenarios: from web applications, up to banking transactions.


AI-driven testing


Data-driven test modelling

Using advanced Python and ML libraries, the agent collects and analyses data from various sources (such as requirements documents, technical specifications, etc.), setting the stage for accurate modelling and analysis.


Automated test design generation

Employing LLMs the agent synthesises data to create comprehensive test designs, ensuring adherence to ISTQB standards for high test quality.


Customised test script creation

Leveraging Python, NLP, and Computer Vision, the agent generates tailored test scripts, enhancing the testing process's efficiency and accuracy.


Efficient test suite execution

The agent’s smart automation capabilities enable the execution of sophisticated test suites with standard test automation engines


Intelligent reporting

Gain valuable insights from testing results through detailed and feature-level quality metrics, enabled by advanced Machine Learning and Deep Learning technologies.


The Generative AI test design process

In the Generative AI test design process, the testing assistant agent employs a LLM model fine-tuned with Reply Quality Assurance best practices. This approach enables the creation of test books in a structured format, ensuring compliance with market-leading test management tools. Additionally, the agent facilitates automated test coding, significantly enhanced by the use of a Copilot, and enriches test report generation with both detailed and feature-level quality metrics.

Feedback and advancements in test execution are then contextualised with respect to the overall process being tested. This procedure marks an evolution from basic to advanced testing, leveraging data-informed strategies and customising testing journeys to meet the specific requirements of diverse software applications.


Speed and testing quality

Reduced test development time

Drastic decrease in time needed for test list and script creation.

Improved test quality

Enhanced precision in test lists and code quality through AI application.

Streamlined requirement mapping

Effective alignment between test cases and requirements.

Quicker market readiness

Acceleration of the overall testing timeline, adaptable to various delivery scenarios.


Concept Reply, part of the Reply Network, is a leader in IoT solutions. They support and advise customers across Automotive, Manufacturing, Smart Infrastructure and beyond in all aspects of Internet of Things (IoT) and Cloud Computing. From designing and developing customized IoT solutions to implementing and managing them seamlessly, Concept Reply helps customers unlock the potential of IoT.