Our Product Verification and Validation Services focus on two fundamental questions:
- Validation: Are we building the right product?
- Verification: Are we building the product right?
CSS Corp’s domain expertise, proven methodologies, and processes ensure that the right product is built in the right way, to help you win customer trust and achieve brand loyalty. Our V&V team of 300+ testing professionals has significant expertise in end-to-end testing in areas including web, mobile, and legacy. We have successfully completed 1500+ engagements with OEMs and ISVs, and ensure continuous cost optimization through our flexible engagement models and analytics-driven governance.
For organizations to stay current with the upcoming trends, features are included in the product and faster GTM is the need of the hour. Organizations spend too much time in regression testing to ensure the product does not break. While regression testing is a need, the definition of ‘how far’ and ‘how much’ has been debatable. Some of the challenges include complex product development environments, multiple re-verifications, and increased maintenance costs, balancing Coverage/Priority/Business criticality, silo functioning of various teams (Development, QA, and Business).
CSS Corp has evolved an optimization approach to address the above challenges which use the machine learning techniques such as logistics regression, support vector machines, multi-layer perception etc. in combination with meta-heuristic algorithms such as ant colony, grey wolf etc. to arrive at compact and effective regression approach.
Some of our key differentiators are:
- Reusable Test Scenarios Repository: Ready-to-use repositories, toolkits and utilities, which optimize testing efforts while reducing time-to-market by 15-20%.
- NAIL IT: Early validation tool that runs on Natural Language Processing (NLP) engine to detect errors, defects, ambiguity, and complexities.
- Go-No-Go decision framework: Framework based on expertise from numerous engagements on what and when to automate.
- Hybridizing Meta-heuristic with Machine learning: Prioritizing requirements/test scenarios using meta-heuristic algorithms with machines learning for an optimal utilization of resources.
- Fractal-based ROI analysis: Framework for measuring the three dimensions of ROI – tool, effort, value.
