Fragmented test reporting paralyzes your QA process
Modern software teams work with a wide range of test tools, build tools, and security scanners. Each tool brings its own reporting format. The outcome? A fragmented landscape in which QA leads, testers, and developers spend hours deciphering test results from different sources. Without central cohesion, patterns remain hidden, defects go unnoticed, and analysis takes unnecessarily long.
This is an obstacle to an effective quality assurance process. The lack of overview slows down defect analysis and frustrates every attempt at structural QA process optimization.
Tools that are widely used but known for unclear or fragmented reporting include:
- Jest, JUnit, pytest, NUnit (unit testing): output often CLI-based, inconsistent across tools
- Selenium, Cypress, Playwright, Puppeteer (E2E): proprietary HTML reports without central correlation
- Postman, SoapUI, Karate (integration testing): per-call logs, hard to tie to test flows
- JMeter, K6, LoadRunner (performance): raw CSVs or charts without context on functional impact
- OWASP ZAP, SonarQube, Snyk, Burp Suite (security): security output often technical and hard to interpret
- CI/CD logs from Jenkins, GitLab CI, Azure DevOps: not synchronized with other test results
Modern teams are also using more and more tools. Whereas in 2020 organizations used an average of 6 different test tools, by 2025 that has grown to more than 15 on average.
This growth hinders a smooth quality control process: it leads to context switching (an average of 23 minutes needed to refocus), 40% productivity loss from switching between interfaces, and a higher likelihood of errors due to incomplete context.
The hidden costs of reporting chaos
The fact that these tools lack a uniform reporting option results in less overview and insight, ultimately leading to hours of extra investigation. Work we used to accept because we didn’t know any different. But those extra hours cost organizations a lot of money in resourcing. They go into:
Time wasted on manual correlation:
Test teams spend 3–4 hours a day piecing together reports:
- Logs from Jenkins, GitHub Actions, or Azure DevOps
- Failing test cases from Cypress or Playwright
- Integration failures in Postman
- Performance data from JMeter and K6
- Security alerts from SonarQube and OWASP ZAP
Missed defect patterns:
Due to data fragmentation it’s nearly impossible to:
- Recognize recurring issues across tools
- Analyze root causes that span multiple layers
- Understand the impact of code changes on different test types
Increased time to resolution:
Without integrated reporting it takes:
- Time to switch between tools
- Insight to locate errors in time
- Energy to keep teams aligned based on incomplete data
These inefficiencies run counter to modern quality assurance principles and form a structural obstacle for organizations striving for continuous improvements in their QA process.
Orangebeard solves the fragmentation
Orangebeard turns test chaos into insight. The platform aggregates test results from all test tools and standardizes them into a single, clear model. This helps organizations achieve effective quality control and sustainable QA process optimization.
The current integrations can be found at orangebeard.io/kennisbank/integraties.
Orangebeard then converts all test results into a uniform model:
- Test results and runtimes
- Error messages and stack traces
- Coverage information and traceability
- Historical trends and failure frequency
- Environment and configuration information
One dashboard. One source of truth. One integrated view of test quality. With Orangebeard, teams no longer have to scroll through 12 tabs and PDFs, manually match timestamps, and filter out unnecessary context from irrelevant tools. And that results in faster releases and more focused QA cycles.
Improved defect detection and root cause analysis
By bringing together data from multiple layers, significant advantages arise for the quality assurance process:
- Correlation between API changes and UI failures
- Discovery of trends such as “code rot” or deteriorating performance
- Clear impact analysis of defects across test types
- Shared insight into real-time data
- Standardized issue classification
- Direct traceability to builds, code changes, and owners (audit trail)
Unified test analytics
Orangebeard enables analyses that are impossible with standalone tools, while you keep working with what you already use. Teams can therefore stick with their preferred tools, and new tools are easy to add. All test reports are shown in a single dashboard.
But there are more benefits to be gained with Orangebeard, such as:
Predictive failure analysis
Predicts fragile components before release
Optimizes test selection based on risk and impact
Quality trend intelligence
Uncovers long-term quality issues
Correlates velocity and defect rates
Measures the effectiveness of test strategies over time
Resource optimization
Identifies test types with the highest defect likelihood
Shifts resources from redundant tests to high-yield coverage
Plans automation based on proven ROI
From fragmentation to intelligence
Fragmented reporting isn’t just inconvenient; it’s costly, risky, and inefficient. Orangebeard transforms reporting from a reactive chore into a proactive weapon for quality improvement. Less time spent on analysis. More insight. Better collaboration. And above all: faster and smarter delivery.
The future of testing isn’t only automated. It’s integrated, intelligent, and immediately action-oriented. Take the step today toward better quality assurance with Orangebeard.
From chaos to insight
The road to integrated test reporting starts with a central platform. Orangebeard offers:
- Full integration with your current tool stack
- Standardization of all your test results
- In-depth analyses and real-time dashboards
- Plug-and-play onboarding without tool lock-in
- Real-time analyses by multiple team members simultaneously
- Dashboards that proactively visualize trends
- Ask the AI Test Assistant about test status and risks in plain language