AI Co-Pilot for Test Suite Intelligence and Optimization

The Five Problems Slowing the Dev-Test Iterative Loop

Tests are a significant component of the DevOps infinity loop. Every commit has to pass a series of test suites (unit, functional, regression, integration, smoke, UX, etc.) before it is certified as good enough to reach production. The workflow restarts if a test fails. Test suites grow over time and add execution time, slowing the delivery velocity.

Long runtimes for tests that slow feedback signal to devs and QA

80% of tests are pointless for a given set of incoming changes—you don’t know which ones.

Thus, QA runs all the tests, spending time/effort and money.

The result is slow development velocity.

Noise generated by Flaky & Unhealthy tests that take effort to sift through

Every test suite suffers from flaky and unhealthy tests.

QA lacks data insights to know which ones cause the most friction.

Eventually, developers lose trust in the test suite.

Manual root-cause analysis of test failures slows down dev-test iteration

There is a firehose of test failures from every test session.

QA manually goes through test failures to determine root-cause analysis and assign owners.

There is a never-ending pressure to be constantly ahead of failures while lacking tooling that helps.

Test failure awareness propagates slowly, causing delays in root-cause analysis

Devs & QA poll CI tools or skim emails to determine if they broke post-merge builds.

Regular fire drills to raise awareness of issues.

Tracking of issues and fixes

As test failures happen, the real work of tracking fixes starts.

QA jumps between test failure reports and the bug tracking system to keep track of what’s new and what’s fixed.

Tracking issues at individual test failure levels is cumbersome and time-consuming.