Testing and Continuous Integration (CI)
We implement modern testing strategies and continuous integration pipelines to ensure software reliability, fast feedback loops, and development efficiency.
We help you deliver secure software faster, with compliance built in. We help software teams deliver faster without compromising on security, compliance, or resilience. from startups to NIS2-regulated enterprises, our pipelines embed security and testing at every stage.
Our security-first CI/CD approach ensures that time-to-market, compliance, and risk management are never in conflict. By embedding advanced testing and monitoring throughout the pipeline, we help you deliver software that is faster, safer, and auditable, without slowing down innovation.
Our integrated testing framework directly supports business outcomes that matter to you:
- Accelerated delivery: vulnerabilities are caught early, avoiding costly late-stage fixes.
- Compliance assurance: processes support ISO 27001, GDPR, SOC 2, and NIS2 readiness.
- Lower total cost of ownership: reduced rework, fewer breaches, and minimized downtime.
We layer multiple security scans throughout the CI/CD pipeline to “shift left” on risk detection, ensuring issues are resolved early and efficiently:
- Static Application Security Testing (SAST): Embedded at commit time, SAST automatically scans code, bytecode, and binaries for common vulnerabilities (for example, SQL injection, XSS, buffer overflows). Developers get immediate feedback within their workflow, enabling quick remediation.
- Dynamic Application Security Testing (DAST): In staging, DAST simulates real-world attacks on running applications to detect authentication flaws, server misconfigurations, and injection vulnerabilities that appear only at runtime.
- Software Composition Analysis (SCA): Our SCA tools continuously monitor third-party libraries and open-source components for known CVEs, outdated dependencies, and licensing issues, protecting you from supply chain risks.
Enterprise leaders need more than secure code—they need visibility and control. Our reporting and dashboards give managers and project leads a real-time view into vulnerability trends, remediation times, and compliance alignment. This ensures security is not just enforced but also auditable and boardroom-ready.
We don’t just claim results, we measure them. Our KPIs demonstrate how security strengthens delivery, not hinders it:
- Critical vulnerability detection: <15 minutes from code commit
- Critical issue resolution: <2 hours on average
- Pipeline security gate pass rate: >95%
- False positive rate: <10%
- Vulnerability trend: month-over-month reduction
Vulerability detection
Pipeline security gate pass rate
Issue resolution
False posisitve rate
Accurate end-to-end testing with sandboxed environments
Our E2E testing strategy implements comprehensive testing ecosystems that simulate real-world conditions without impacting production systems or external services.
We deploy complete application replicas with real user interfaces and API endpoints that capture exactly the behavior of live systems. This allows us to test complex, multi-step workflows, such as authentication flows, payment processes, and business-critical approvals, without exposing production data or systems. Teams gain confidence knowing every user journey has been validated under near-real conditions.
To validate communications without risk, we use specialized e-mail testing frameworks that capture and analyze outgoing SMTP messages in controlled environments. This ensures workflows such as account registration, password resets, and transactional notifications are thoroughly tested without sending messages to real users.
Modern enterprise applications depend on external services. Our API mocking frameworks simulate integrations with payment providers, identity systems, and third-party APIs—including error handling, rate limiting, and timeout conditions. This ensures robust testing coverage while eliminating reliance on external service availability, thereby reducing both cost and complexity.
We measure success with KPIs that align testing outcomes with business-critical objectives:
- E2E test coverage: >90% of critical user journeys covered
- Test environment uptime: >99.5% availability ensured
- Execution time: Complete suite runs in <20 minutes
- Data isolation: 100% separation between concurrent test runs
- Integration coverage: All external API connections tested under controlled conditions
E2E test coverage
Execution time
Test environment uptime
Data isolation
A leading automotive supplier reduced remediation time from 3 days to 2 hours by adopting our integrated CI/CD & testing approach.
Detailed stress testing
Our stress testing program validates performance limits, exposes failure modes, and confirms that autoscaling and infrastructure behave as designed before peak events hit production.
What we test
We utilize cloud-ready benchmarking frameworks, such as Gatling, to generate realistic, high-volume traffic from multiple regions. Tests are codified and repeatable, ensuring that results are comparable over time and across different environments. We model step, spike, soak, and breakpoint runs to find the actual capacity ceiling and the shape of degradation.
We target the business-critical paths that drive revenue and user experience: authentication, checkout, search, pricing, document generation, and data export. For each path, we drive concurrent load through the exact endpoints, queues, and database queries involved, capturing contention, hot locks, n+1 patterns, and cache miss behavior that only appear at scale.
We validate that the cloud-native implementation can scale in time, not just in theory. Using your IaC (Terraform, Pulumi, Helm, Flux CD, Argo CD), we run controlled scale-up and scale-out drills to prove that autoscaling groups, HPA/VPA, node pools, serverless concurrency, and messaging tiers can add capacity quickly enough to meet demand. We measure cold-start impact, image pull time, database read replica spin-up, and changes in provisioned throughput, and confirm rollback paths in case scaling fails.
- Load modeling: Derive arrival rates and traffic mixes from production telemetry, applying diurnal patterns and regional skew.
- Controlled environments: isolate runs in dedicated cloud projects/accounts with production-like data shapes and feature flags.
- Observability first: correlate load with golden signals (latency, traffic, errors, saturation), flame graphs, and query plans.
- Failure injection under load: introduce network jitter, dependency timeouts, and instance terminations to validate graceful degradation.
- Cost visibility: track cost per request at peak and during headroom bursts to avoid scaling that solves latency but explodes spend.
- Throughput at contractual SLO: sustained RPS with p95 latency.
- Headroom: at least 30% additional capacity before breaching p95.
- Autoscaling reaction time: the time from a load surge to achieving stable capacity that meets the target (for example, ≤ 3 minutes to double capacity).
- Recovery from spike: time to return to baseline latency after a 10× burst.
- Database and cache health: connection pool saturation ≤ 80%, cache hit rate ≥ target, no hot partitions.
- Queue durability: the backlog drains to a steady state within the agreed-upon time window, with no message loss.
- IaC provisioning reliability: zero failed scale operations during drills; successful rollback if limits are reached.
You receive a concise report with reproducible test plans, scripts, and infrastructure orchestration, as well as bottleneck heatmaps, tuning recommendations (including indices, pool sizes, and cache strategy), and a go/no-go summary aligned with your SLOs and release calendar.
We build chaos experiments on Kubernetes using industry-grade tools such as Litmus and Chaos Mesh, and where appropriate, integrate service-level chaos providers like Gremlin. Litmus gives us programmable chaos experiments as custom resources that fit naturally into GitOps workflows, while Chaos Mesh provides fine-grained network, IO, and time-based fault injection. These tools allow repeatable, auditable experiments that run safely in isolated namespaces or dedicated test clusters.
- Scalability and autoscaling behavior: validate HPA/VPA, node autoscaler, and serverless concurrency under sustained load and sudden surges.
- Node maintenance and lifecycle events: simulate node drains, kubelet restarts, kube-proxy failures, and control-plane latency to ensure planned maintenance and unexpected node loss are non-disruptive.
- Network and dependency failures: inject latency, packet loss, DNS failures, and TCP resets between services to verify graceful degradation and circuit-breaker behavior.
- Stateful workloads and data integrity: test failover of stateful sets, replica promotion, and storage resilience during node outages and volume detach/attach cycles.
- Resource exhaustion: simulate CPU, memory, and file-descriptor saturation to expose contention hotspots and misconfigured limits/requests.
- Downstream and third-party outages: emulate slow or failing downstream APIs, rate limits, and quota exhaustion to validate timeouts, retries, and fallback logic.
- Chaos combined with load: run chaos experiments during load tests to discover correlated failure modes that only surface under pressure.
We select testing frameworks that align with our project’s technology stack, ensuring seamless integration and reduced learning curves.
Our teams regularly assess emerging testing frameworks and tools, conducting proof-of-concept evaluations every quarter to identify potential improvements. This includes analyzing factors such as execution speed, maintenance overhead, community support, and integration capabilities with existing CI/CD infrastructure.
Rather than enforcing a one-size-fits-all approach, we tailor testing strategies to project characteristics, including scale, complexity, compliance requirements, and team expertise. This flexibility ensures optimal ROI while maintaining consistent quality standards across all projects.
We follow a controlled, auditable process for every experiment:
- Hypothesis and expected outcome were defined and reviewed by stakeholders.
- Blast radius scoped with namespace, pod label, and resource limits; requires approvals for production-scope runs.
- Steady-state metrics and guardrails defined (slo thresholds, circuit-breaker trips).
- Automated rollback and abort conditions wired to observability alerts.
- Post-mortem and learning capture are integrated into sprint retros and runbooks.
Experiments feed unified dashboards that correlate injected faults with golden signals and business metrics. We integrate telemetry into Prometheus and Grafana, add traces to Jaeger or Tempo, and present results in executive-friendly dashboards that display experiment outcomes, mean time to detect, mean time to recover, and runbook effectiveness.
Our AIOps platform analyzes code patterns, performance metrics, and security vulnerabilities to provide contextual suggestions for improvements. The system leverages machine learning models trained on best practices and organizational coding standards to recommend optimizations and identify potential issues before they manifest in production.
When security vulnerabilities are detected, our AIOps system can automatically generate patches and security fixes based on established remediation patterns. This includes automated dependency updates, configuration adjustments, and code modifications that address standard vulnerability classes without requiring manual intervention.
Our comprehensive license scanning ensures full compliance with open-source licensing obligations while preventing legal and security risks. We automatically scan all source code, documentation, and dependencies during the build process. Our scanning covers repository contents, third-party libraries, and even checks for potentially conflicting license combinations that could create legal complications.
Our deployment automation ensures consistent, reliable, and policy-compliant deployments across all environment tiers.
Code changes automatically progress through our environment hierarchy following predefined policies. Deployments begin in development environments, proceed to staging for comprehensive testing, and are finally deployed to production only after passing all quality gates and approval processes.
All environment configurations are managed through code, ensuring consistency and repeatability across deployments. Our deployment pipeline automatically provisions and configures infrastructure resources, eliminating configuration drift and reducing deployment-related issues.
Our CI/CD pipeline ensures documentation remains current, accurate, and accessible through automated generation and validation processes.
Using tools like Pandoc and ADOC, our pipeline automatically converts documentation from markdown sources into multiple formats, including HTML, PDF, and interactive documentation sites. This ensures stakeholders can access documentation in their preferred format while maintaining consistency from a single source.
Our stringent code coverage requirements ensure comprehensive testing while maintaining development velocity through intelligent threshold management.
Rather than fixed percentages, we implement dynamic thresholds that prevent coverage regression while accommodating project-specific requirements. New code must maintain or improve overall coverage, with minimum thresholds typically set between 80% and 95%, depending on the component’s criticality.
Favorite tools that we prefer
Our technology stack showcases our proactive stance as early adopters, meaning we actively implement and refine new, cutting-edge solutions before the industry widely adopts them.
Selenium
Katalon Studio
Playwright
Cypress
Gauge
Cloud Browser Stacks
Scala Steward
Renovate
Bitbucket
Github
Azure
Gitlab
Every team’s challenges are unique, and we’d be glad to understand yours. Reach out to discuss how modern CI/CD, integrated testing, and compliance-focused pipelines can be tailored to your organization’s goals.
