Built Automated QA Covering 300+ Test Cases for a Network Platform
Our client operates a mission-critical network management platform used by cable operators to monitor broadband infrastructure serving large residential and commercial customer bases. The platform spans multiple interconnected subsystems – AI inference services, high-scale pollers, analytics pipelines, operational dashboards, and REST APIs, each of which can introduce regressions that are invisible to functional testing but catastrophic under production load.
The organisation needed a unified QA automation strategy that combined functional regression coverage with load testing and infrastructure health validation across all platform components, with the reliability requirements of a network management platform making inconsistent or incomplete release validation structurally unacceptable.
Key Challenges
Manual testing covered fewer than 50 scenarios across a multi-subsystem platform, with no load testing, performance baseline, or consistent release validation process.
01
Incomplete Regression Coverage: Manual testing focused on key UI flows, leaving major portions of the API, poller, and AI infrastructure untested before releases.02
Load Testing Absence: No systematic load testing existed, so performance and scalability issues were often discovered only after reaching production.03
Release Validation Inconsistency: Validation steps varied by deployment and team availability, resulting in inconsistent quality gates and release confidence.04
AI Infrastructure Test Gap: Model loading, inference routing, and feedback capture lacked dedicated test coverage, making AI regressions difficult to detect before release.05
Poller Regression Blind Spot: Polling schedulers, concurrency settings, and CMTS integrations were tested manually and infrequently, leaving scale-related regressions undetected.06
No Performance Baseline: Without established benchmarks, the team could not reliably identify latency or throughput regressions before they affected users.Our Solution
Ksolves, an AI-first technology company offering quality assurance services, built a comprehensive QA automation framework covering every production-critical subsystem, with functional and performance testing integrated into the release process.
- 01 API Regression Test Suite: Automated testing covered major REST APIs, including model, response, feedback, alarm, and topology endpoints, with contract and edge-case validation.
- 02 Load Testing Framework: Built configurable load tests to measure P95 latency and throughput while establishing versioned performance baselines for release comparisons.
- 03 AI Infrastructure Test Suite: Added automated coverage for model loading, inference routing, and feedback storage, making AI regressions detectable early.
- 04 Poller Regression Suite: Automated validation of scheduler behavior, concurrency limits, CMTS integration, and data integrity across multiple polling configurations.
- 05 CI/CD Release Validation Gate: Integrated regression, load, and infrastructure tests into CI/CD as mandatory release gates, ensuring consistent validation across deployments.
Data Flow Diagram
Technology Stack
| Category | Technology |
|---|---|
| Methodology | Automated API Regression Suite |
| Methodology | Load Testing Framework |
| AI/ML | AI Infrastructure Test Suite |
| Processing | Poller Regression Suite |
| DevSecOps | CI/CD Release Validation Gate |
The automated QA framework strengthened release confidence by expanding test coverage, enabling early performance detection, and standardizing validation across deployments.
- Test Coverage Expanded From Fewer Than 50 to 300+: Automated testing now covers 300+ API, AI infrastructure, poller, and load-testing scenarios.
- Performance Regressions Detectable Pre-Release: Versioned baselines and automated load testing identify performance regressions before production deployment.
- 100% Consistent Release Validation: Automated CI/CD gates run the same comprehensive validation suite for every deployment.
- Regression Detection Time Reduced by an Estimated 95%: Automated AI and poller tests detect subsystem regressions within minutes instead of days or weeks.
Conclusion
Ksolves transformed an inconsistent, manual QA process into a standardized, automated testing framework covering APIs, AI infrastructure, pollers, and performance. With 300+ automated test scenarios, versioned performance baselines, and mandatory CI/CD validation, the client can now detect regressions earlier and release with consistent, measurable confidence.
