Quality trend analysis
Have the model analyse defects, incidents and test results over months to find trends — areas that keep regressing, kinds of bugs that escape testing, and whether changes in practice made a difference.
How it works
Individual bugs get fixed; patterns in them rarely get noticed. The model reads defect and incident records over a long period, classifies each by area, type, how it was found (test, review, customer, monitoring) and which release introduced it, and looks for patterns: components with recurring regressions, bug types that consistently reach production, a rise in escapes after a process change. Combined with test-run data it can show whether the areas with the most defects get the most testing. The analysis is presented with the counts and examples behind it, not just conclusions.
When to use it
Retrospectives and quarterly reviews; deciding where to invest in testing; checking whether a process change — a new review rule, more automation — actually reduced defects; and building the case for addressing a chronic problem area.
Limitations
Defect data is inconsistent: fields filled in carelessly, categories that changed over time, bugs logged in different ways by different teams. The model's classification adds its own errors on top. Trends in reported bugs reflect reporting behaviour as much as quality. Correlation in this data is easy to over-read, and the model will offer causal stories readily.
Watch for
Customer-found bugs drop after a process change, but only because support started logging issues in a different tool. Before crediting a trend, check whether how or where bugs were recorded changed at the same time.
What you still own
Interpreting the trends, and treating causal claims as hypotheses to check before acting on them.
Cite this
Qlarify Labs. (2026). Quality trend analysis. Retrieved from https://labs.qlarify.fi/ai-in-testing/quality-trend-analysis