Agivant’s framework uses Large Language Models to translate simple English language to programming input to backend automation foundations. It enables domain experts or business analysts to write test cases using Domain Language.
Generative AI promises to reshape the world of software testing, offering automation, efficiency, and new testing approaches. Deep dive with us as we examines Gen AI tools, benefits, challenges, ethics, and the future of this cutting-edge technology.
Agivant adoption of GenAI and its impact
Agivant’s Generative AI-based shift left testing strategy is to enable business users and developers to identify defects in the system proactively.
Generative AI involves algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders to help synthetic dataset generation. Additionally, transformer architectures have significantly amplified neural networks’ proficiency in handling sequential data, paving the way for complex data generation and analysis.
Agivant’s framework extensively leverages the Generative AI ability of text mining and processing to autonomously create complex, scenario-based test cases, opening new vistas for enhancing testing processes.
The recent advancements in artificial intelligence and machine learning technologies have given rise to a new paradigm in software testing, known as Autonomous Test Generation.
With advanced data science, statistical modeling, Generative AI capabilities, and modern platform engineering capabilities, Agivant has built this unique service offering.
Automated Test Case Generation
Generative AI's ability to autonomously churn out complex, scenario-based test cases significantly reduces the manual effort required in test case generation, accelerating the testing phase considerably. By optimizing code coverage through intelligent test case design, a more thorough validation can be achieved early in the development cycle.
Train the Generative AI model on a dataset of existing test cases and known software vulnerabilities.
Fine-tune the model to understand the software architecture and critical functionalities.
Implement the Generative AI model within the CI/CD pipeline to generate test cases automatically for new code changes.
Synthetic Data Generation
Use of deep learning generative models such as Generative Pre-trained Transformer (GPT) methodology, Generative Adversarial Networks (GANs), and Variational Auto-Encoders (VAEs). These algorithms accelerate learning from existing data and produce new synthetic instances resembling the original dataset. This helps to protect PII data protections, addressing compliances like GDPR.
Predictive Bug Discovery
Generative AI models can predict potential bug-generating patterns besides analyzing code. This feature enables the generation of intricate test scenarios to validate and rectify code, thereby reducing bug discovery and fixing times.
Train the Generative AI model on a dataset of existing test cases and known software vulnerabilities.
Enable the model to analyze new code and provide feedback on potential bug-prone areas
Integrate this feedback into the development process for proactive bug prevention
Using Plain English Text to reduce dependencies
Agivant’s framework uses Large Language Models to translate simple English language to programming input to backend automation foundations. It enables domain experts or business analysts to write test cases using Domain Language.
Cognitive Automation
Gen AI brings cognitive automation to software testing, allowing machines to mimic human cognition. This includes understanding complex requirements, identifying patterns, and adapting to changes in the application's behavior. Cognitive automation significantly reduces the manual effort required for test case design and maintenance.
