Incedo AI-Driven Intelligent Regression for Hi-Tech

Reinvent Regression Testing for Modern Software Delivery

Release speed should not depend on running thousands of tests that add little value. Yet many enterprises still rely on large, repetitive regression suites that slow CI/CD pipelines, increase infrastructure cost, and delay feedback to engineering teams.

Incedo AI-Driven Intelligent Regression helps organizations modernize quality engineering with an AI-first approach to smarter test execution. Instead of executing every test in every cycle, the platform determines what truly needs to run, what should run first, and how testing should continuously improve over time.

The result is faster releases, better defect detection, lower testing cost, and a more intelligent path to software quality.

Why Traditional Regression Models Break at Scale

As applications grow in complexity, regression suites expand into thousands of test cases across products, platforms, and release trains. Full test cycles can take hours or days, creating bottlenecks across development and release management.

At the same time, many executed tests are redundant because they are unrelated to recent code changes. Critical scenarios may run too late in the cycle, delaying defect discovery and increasing production risk.

Teams are forced to balance speed, cost, and quality—often sacrificing one to protect the others.

How the Platform Creates Value

The platform uses a dual-optimization model to make regression testing faster and smarter.

First, intelligent change impact analysis maps code changes to affected modules, components, and functional areas to identify only the tests relevant to the current build. This reduces unnecessary execution while preserving confidence in impacted areas.

Second, AI-based prioritization applies reinforcement learning to historical failures, defect density, production issues, and change frequency to rank selected tests by business risk and failure probability. High-value scenarios run first, enabling earlier detection of critical defects.

With every cycle, the platform learns from new outcomes and continuously improves future recommendations.

Built Using AWS Services

The solution is built on AWS and can leverage Amazon SageMaker for machine learning models and reinforcement learning, Amazon Bedrock for generative AI insights and test intelligence, AWS Lambda for workflow automation, Amazon S3 for execution history and artifacts, Amazon Redshift for analytics workloads, Amazon QuickSight for quality dashboards, AWS CodeBuild for CI/CD integration, and AWS Identity and Access Management for secure access controls.

What This Enables

Organizations gain shorter regression cycles, reduced test execution volume, earlier defect detection, smarter release decisions, lower QA infrastructure cost, continuous optimization of testing strategies, and a scalable quality engineering foundation for modern delivery teams.

Why It Matters

With intelligent regression in place, enterprises can accelerate time-to-market, improve release confidence, reduce production defects, optimize QA spend, and build a software delivery model that scales with speed and complexity.