Unlock the Full Potential of Your Software Development with AI-Assisted Testing
AI-assisted testing optimizes software development by improving test quality and speed, enabling cost savings, and automating the creation of test cases, test data, and test documentation.
We identify optimization potentials through AI in your testing process, helping you detect errors as early as possible in the development process ("shift-left testing").
Testing in software development typically accounts for 20% to 40% of the total development time. We implement AI-assisted approaches to automate and reduce the effort involved.
We customize AI-assisted testing to your individual needs, with the goal of optimizing your test coverage and improving test quality.
Every client has unique requirements, so we adapt existing AI-assisted testing solutions to meet your specific needs. Our focus is on cost reduction through shift-left testing, efficiency improvement through test automation, and the optimization of test coverage and quality.
Our approach to AI testing combines advanced technical principles with cutting-edge technology. By integrating Retrieval Augmented Generation (RAG), we significantly improve the accuracy of responses by seamlessly incorporating external knowledge into LLMs (large language models). This allows us to create precise, context-aware automated tests, test data, and documentation.
In addition, our methodology incorporates reinforcement learning with AI feedback (RLAIF), continuously refining our models to minimize human intervention. This innovative approach accelerates development cycles while ensuring the reliability and robustness of AI-assisted test tools, making them adaptable and future-proof for evolving business needs.
Tailored consulting services aligned with your strategic goals and regulatory requirements.
With our comprehensive approach, we guide you through the entire test pyramid, combining expert consulting with technical precision.
Modern LLMs, such as GPT-4, process more than just words. AI-assisted UI testing tools can now be developed using application screenshots to provide actionable insights.
Utilizing an LLM with Retrieval Augmented Generation (RAG) to generate test cases that are then automatically executed on the API, followed by automatic reporting and analysis of expected versus actual responses.
Generating anonymized test data that contains necessary patterns and correlations for proper testing can be time-consuming. Modern LLMs help create meaningful and usable test data.
Our services are designed to enhance your testing processes through AI-assisted tools, focusing on the shift-left testing approach. We help you reduce costs by implementing a testing system that detects errors early and prevents costly issues during system tests or release phases.
We evaluate your current testing process and identify potential areas for AI implementation, along with metrics to measure efficiency improvements through AI-assisted testing. This results in an overview of efficiency potentials and key impact metrics, accompanied by a cost analysis.
We select the most suitable tools for your needs and integrate them into your existing processes as part of a proof of value. The outcome is a concrete implementation of AI-assisted testing tools for a specific case and a detailed cost-benefit analysis based on predefined metrics.
We develop a roadmap for company-wide rollout and operational procedures for implementation and maintenance. We also provide support in development, system integration, and architecture.
As the scope and complexity of software systems continue to grow, the need for more effective software testing techniques becomes increasingly urgent, making this area ripe for innovative approaches like the use of LLMs.
Testing remains a critical part of the software development process. AI-assisted testing tools provide a powerful lever to optimize test coverage, accelerate test cycles, and improve cost efficiency.
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