Automation Framework Development for Genomic Data Analysis and AI Application Testing

The client develops AI-based healthcare applications utilizing genomic data, which require precise DNA sequencing, compliance with strict privacy standards, and robust handling of complex business rules. Manual testing processes are time-consuming and inadequate to cover the expanding suite of applications, leading to increased QA cycle times and potential quality risks. Lack of specialized automation frameworks tailored for genomic and AI model testing hampers efficient release cycles.

A mid-to-large scale healthcare technology company specializing in AI-driven genomic analysis and personalized medicine solutions, with a focus on accelerating drug development and disease diagnosis.

• Reduce overall testing cycle time by automating regression and critical test cases, aiming for a measurable decrease similar to a 95% reduction.
• Achieve at least 80% automation coverage across key applications such as genomic data management systems, laboratory information management systems (LIMS), and reporting pipelines.
• Accelerate development and deployment processes by automating initial login, user role validations, and data validation routines, reducing manual effort significantly.
• Enhance test reliability, accuracy, and coverage through frameworks capable of handling complex AI models and genetic data validation.
• Integrate automated testing seamlessly into existing CI/CD pipelines to support continuous delivery with minimal disruption.
• Implement data-driven testing approaches to ensure broad scenario coverage across various user roles, insurance options, and data inputs.

• Automation of over 900 manual test cases across multiple applications using tools like Playwright, with capabilities for API, UI, and data validation testing.
• Enhanced automation framework supporting genomic data processing, including functions for data quality checks, trimming, and integrity validation.
• Support for data-driven testing with flexible input datasets to cover diverse scenarios such as user roles, data states, and feature variations.
• Robust APIs for interacting with genetic data, managing credentials, handling authentication mechanisms like OAuth/JWT, and storing test states.
• Utilities for genomic data prefix trimming, read quality filtering, and sample tracking to ensure comprehensive test coverage.
• Logging, reporting, and traceability features to facilitate debugging and continuous improvement.

• CI/CD pipelines for continuous testing and deployment
• Genomic data repositories and management systems
• APIs for genomic data processing and AI model validation
• Authentication and authorization systems for secure access management

• High scalability to support increased test volume as genomic data grows
• Performance benchmarks ensuring test execution times are minimized, targeting rapid feedback cycles
• Security compliance with HIPAA and HL7 standards for patient data
• Reliability and robustness to reduce false negatives/positives in testing results
• Maintainability through modular design and detailed documentation

The implementation of a comprehensive, automated testing framework is anticipated to reduce manual testing efforts by over 90 hours per cycle, achieve up to 80% automation coverage of critical applications, and decrease QA cycle times by approximately 95%. This will enable faster development and deployment of AI-powered genomic applications, improve test reliability, and facilitate continuous integration, ultimately supporting the client’s mission to advance precision medicine efficiently and reliably.