QA Automation

CodiLime combines containerized testbed orchestration (using Docker and Kubernetes) with dynamic network emulation and physical hardware integration. This approach allows us to execute parallel test topologies on shared compute resources on demand, reducing physical lab overhead by up to 60% while expanding daily test execution capacity tenfold.

Yes. CodiLime teams build modular test suites using Pytest, Robot Framework, Python, and Go that run in standard CI/CD engines such as Jenkins and GitLab CI. We integrate with commercial traffic generators such as Ixia and Spirent, and build custom open-source high-throughput engines such as TRex 200 Gbps.

CodiLime implements modular framework architectures, smart test selection based on code change impact, and automated failure classification (such as XFail tagging algorithms). By automatically identifying known non-blocking issues and deterministic failure patterns, we accelerate root-cause analysis from days to hours and prevent transient failures from delaying delivery pipelines.

Our QA engineers are software developers and networking domain specialists first. CodiLime brings deep, hands-on expertise in core protocols (BGP, OSPF, VXLAN, MPLS) and cloud-native architectures, enabling us to build scalable automation platforms, custom traffic tools, and dynamic environment orchestrators.

CodiLime utilizes Agentic AI models and MCP (Model Context Protocol) servers to analyze infrastructure telemetry, assess code impact, dynamically expand test coverage during anomalies, and perform automated root-cause analysis.

We usually start with a 2- to 3-week technical audit of your existing infrastructure, test scripts, and pipeline bottlenecks. We then build a targeted PoC or MVP such as an automated lab provisioning pipeline before scaling up full test suite development and transitioning knowledge to your team.

CodiLime constructs both single-variable and multi-variable scale tests frameworks following RFC 2544 standards. By leveraging high-throughput engines like TRex alongside custom control-plane traffic generators, we stress-test data plane throughput, latency, and dynamic routing protocol convergence under load to catch subtle scaling bottlenecks before code hits production environments.

We build automated end-to-end regression suites that combine synthetic control plane traffic injection with automated state verification via telemetry and APIs. By comparing running states against desired Infrastructure-as-Code (Terraform/Ansible) definitions, our frameworks automatically detect configuration drift, protocol convergence delays, and asymmetric routing issues across leaf-spine and other topologies.

Yes. We automate functional and security validation for east-west traffic isolation, security policy enforcements, and firewall controller APIs. Using automated test engines, we simulate distributed workload traffic to verify that dynamic policy updates do not introduce security gaps, unintended drop rules, or performance bottlenecks.

To eliminate manual maintenance of controller API interactions, CodiLime builds custom SDK generators that automatically create and update client libraries directly from controller API specifications. This auto-generation approach ensures seamless compatibility with new fabric orchestrator releases and drastically cuts ongoing framework maintenance costs.

CodiLime designs automated hitless upgrade validation workflows that monitor packet loss, session persistence, and convergence speed during simulated switch reboots and software upgrades. By combining high-precision traffic generation with real-time observability tools (Prometheus, Grafana, Splunk), we measure exact disruption metrics down to the millisecond to confirm whether failover stays under a second before production deployment.