E-Commerce Testing Success:
Measurable QA Results Across 6 Projects
In the first half of 2026, our work across six large-scale e-commerce projects delivered measurable gains in test throughput, automation and defect management.
*Each figure represents the strongest Q1-to-Q2 change recorded for that metric across the six projects. The figures come from different projects and do not represent a single portfolio average.
Working as an outsourced QA partner, ErikLabs embedded into the testing teams of six large-scale e-commerce and retail operations, each with its own release cadence, technology stack and level of test maturity. We took responsibility across key quality workflows and tailored how performance was measured to the realities of each project.
Key Challenges
The six projects differed in technology, release cadence, active testing days and test maturity, so raw totals did not provide a fair basis for comparison. The same metric could point to genuine quality improvement in one project and hidden coverage loss in another. Four main challenges shaped our approach:
Making Performance Comparable: Active testing days, release frequencies and test volumes varied from project to project. Comparing raw totals alone could distort the true performance of individual teams.
Hidden Loss of Test Coverage: A defect resolution rate could rise while fewer tests were being executed and fewer new test cases were being created. A seemingly positive result could therefore mask a significant reduction in test coverage.
Reading Automation Growth Accurately: New automated test cases and maintenance work on the existing suite needed to be tracked separately. Combining them made it harder to see how much new test coverage was actually being added.
Distinguishing Data Gaps from Performance Changes: Missing baseline data and test execution counts falling to zero could indicate a change in scope, an operational interruption or a reporting gap. Identifying the difference was essential for interpreting Q1-to-Q2 performance correctly.
The ErikLabs Approach
To make performance comparable, surface hidden risks and build a clearer view of quality across the six projects, we applied five core practices:
Project-Specific Baseline Analysis: We established a separate baseline for each project, normalising differences in measurement periods and tracking performance within the context of each operation.
Daily Performance Metrics: We prioritised daily test creation and execution metrics over raw totals, enabling more meaningful comparisons across projects with different schedules and release cadences.
Separation of Manual, Automation and Maintenance Work: We tracked manual test creation, automation development and maintenance workloads separately, with tailored strategies for regression-heavy projects.
Multidimensional Defect Analysis: We evaluated defects across volume, density, severity and resolution speed to build a more complete picture of quality performance.
Proactive Risk Tracking: We treated bottlenecks, sudden performance drops and data gaps as risk signals, allowing teams to investigate issues before they obscured the true state of quality.
Key Results Across Projects
Project-Level Risk Signals
Shrinking Test Coverage: In one project, the defect resolution rate increased from 68% to 90%, a rise of 22 percentage points. Over the same period, test executions fell by 87.7% while defect density increased nearly tenfold. In two other projects, new test case creation declined sharply or stopped altogether.
Reporting and Resolution Bottlenecks: In one project, web test executions fell from 971 to zero, with no Q1 defect baseline available for comparison. In another, defect volume declined while the resolution rate remained at 34.8%, signalling a continuing bottleneck in the resolution process.