GlobalLogic InteliQ

Identifies critical issues and high-risk areas earlier in regression testing through highly effective test case prioritization


GlobalLogic's InteliQ accelerator applies a machine learning approach to regression testing in order to more effectively prioritize test cases and therefore identify critical issues and high-risk areas earlier. The solution also helps test engineers automate manual processes, identify problematic autotests, and detect any outliers that could create a weakness in the development process. By automating QA and detecting defects earlier, InteliQ can reduce project phase costs by around 11% and accelerate the regression test cycle timeline.

Supported Platforms

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Industry Agnostic

Technologies/Works well with

Python, Scikit-learn, Pandas, NumPy, Angular, Flask, GitHub, Terraform, Amazon / compatible with Azure and GCP, Amazon DynamoDB, Amazon S3 , AWS Code Commit, Amazon ECS, AWS Fargate, Amazon Route 53, Amazon CloudFront

Business Needs

Define the most important and highest risk tests (e.g., areas, features), from the point of criticality to product release

Find the most important defects at the beginning of regression testing

Save time and reduce costs for testing without compromising the quality of the product

Estimate risks in advance of the next development cycle to mitigate risks

Identify high-risk tests as candidates for automation

Value Proposition

Creates priority heatmaps for regression to optimize the test run

Helps define manual test case candidates for automation

Eliminates failure risks for automated and manual tests

Highlights risk factors and mitigates project risks

Detects outliers and potentially unstable autotests

Reduces the project phase costs by about 11%


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