AI testing and quality assurance for the development of artificial intelligence
Artificial intelligence, especially machine learning, places special demands on software testing. Even companies with experience in traditional software development must adapt if they want to use the technology successfully.
While traditional software works deterministically, AI systems are based on statistical models. Their behavior is derived from training data and probabilities, which means that their output cannot be predicted with certainty. Statistically balanced AI testing is therefore essential to ensure the quality, reliability, and validity of AI systems.
Additionally, large amounts of data are required for training and testing. The quality of this data directly influences the performance of the model. Inadequate data quality, distortions, or a lack of statistical representation can significantly impair the quality of AI systems. ITPower Solutions supports you in systematically testing and validating AI-based components, thereby increasing their quality.
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Our Services: Quality Assurance and Testing for Artificial Intelligence
MLOps-maturity assessment and improvement
- We analyze your existing MLOps capabilities and develop targeted strategies to improve your processes and systems—tailored to your individual maturity level.
KI-Governance with ISO 42001
- We support you in introducing and maintaining AI management systems in accordance with ISO 42001. This increases transparency and trust in your organization and ensures the responsible operation of AI systems.
Testing methods for AI systems
- Testing AI systems requires a combination of proven methods from classic software testing and specially adapted approaches for AI models. Using our systematic process model, we work with you to develop systematic test concepts and methods based on statistical procedures that enable objective evaluation. The goal is comprehensible quality assurance for AI that is tailored to the characteristics of your system.
Selection and evaluation of training data
- The systematic analysis and selection of suitable training data for AI are crucial for the performance of a model. We support you in evaluating data in terms of quality, completeness, and statistical balance. Through targeted measures, we avoid bias in AI systems and ensure that distortions in AI models are detected at an early stage.
Domain-specific optimization of AI systems
- In order to test AI systems in a targeted manner, their capabilities and limitations must be clearly defined. On this basis, relevant test areas can be narrowed down and prioritized. This allows us to lay the foundation for sound AI validation and realistic evaluation of model performance under defined conditions.
Test case creation and test selection
- Meaningful test results require suitable test cases. We create and prioritize test cases based on statistical criteria and define clear test end criteria. The goal is to enable a robust evaluation of AI models with an appropriate number of tests and to present the quality of the results transparently.
Test data generation
- In addition to real data sets, we rely on test data generation using simulated and synthetic data. This enables us to provide statistically representative test data for AI that is highly informative. At the same time, we support data quality for AI by supplementing and balancing data in a targeted manner.
Test automation
- Automated tests are a central component of modern AI testing strategies. Test automation for AI systems makes test runs objective, repeatable, and efficient. This reduces manual effort, saves time, and enables continuous quality assurance throughout the entire development cycle.
Simulation for AI-Training und Valitation
- In collaboration with Fraunhofer FOKUS, we offer a simulation environment for training and validation. This generates realistic 3D scenarios and input data to train and test AI-based perception systems and supports you in the secure and scalable development of AI-based perception systems (camera, LIDAR).
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Your Benefits: Systematic AI Tests
Benefit from our experience in AI testing and create a robust foundation for high-quality, trustworthy AI solutions.
- Rapid market readiness: Through efficient validation and optimization of ML models
- Cost efficiency: Reduction of costs for data collection, annotation, and testing, as well as reduction of expenses for compliance, audits, and regulatory adjustments.
- Higher accuracy: Improve model performance through optimized training and test data
- Flexibility and scalability: Adaptation to specific use cases and problem-oriented expansion
- Regulatory certainty: Early adaptation to the AI Act and European AI regulations to minimize compliance risks
Qualification and Standards for Systematic Testing of AI Systems
Structured AI testing requires the consistent application of established standards, sound qualifications, and the use of suitable tools. Our approach is based on recognized software testing standards, current guidelines for artificial intelligence, and proven tools for quality assurance, data analysis, and test automation of AI systems.
Norms and standards for AI
- Guidance based on international standards such as ISO/IEC 25010 (software product quality), ISO/IEC 29119 (software testing), ISO/IEC 23894 (risk management for AI), ISO/IEC 42001:2023 (Management System for Artificial Intelligence – AIMS), the EU AI Act, ISO/IEC 24027:2021 (Bias in AI Systems) and guidelines from ISO/IEC JTC 1/SC 42 (Artificial Intelligence) for structured quality assurance for AI systems.
Certifications and qualifications
- Professional qualifications through recognized certifications in software testing and quality management, e.g., ISTQB® Certified Tester, ISTQB® AI Testing, as well as continuous training in the areas of machine learning, AI validation, and data analysis.
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