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Code Bugs in DevOps

Code Bugs in DevOps

$247.00
Toolkit Included:
Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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Course access is prepared after purchase and delivered via email
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What does the Code Bugs in DevOps course cover?

Code Bugs in DevOps is covered here in 8 modules: Integrating Static Application Security Testing (SAST) into CI/CD Pipelines, Managing Technical Debt in Automated Testing Frameworks, Debugging Distributed Systems in Production and 5 more. The outline lists 48 specific topics, opening with decide whether to fail builds on critical SAST findings or allow overrides with documented exceptions based on risk severity and.

How do you approach Code Bugs in DevOps step by step?

The work is sequenced in 8 stages. It starts with Integrating Static Application Security Testing (SAST) into CI/CD Pipelines, moves through Managing Technical Debt in Automated Testing Frameworks and Debugging Distributed Systems in Production, and ends at Diagnosing Performance Regressions in Deployment Pipelines. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Code Bugs in DevOps course?

Module 1 is Integrating Static Application Security Testing (SAST) into CI/CD Pipelines. It works through decide whether to fail builds on critical SAST findings or allow overrides with documented exceptions based on risk severity and remediation timelines., configure SAST tools to analyze only changed files in pull requests to reduce false positives and improve developer feedback speed., implement policy-as-code rules to standardize.

How is the Code Bugs in DevOps course delivered?

The Code Bugs in DevOps course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Code Bugs in DevOps course cost?

The Code Bugs in DevOps course is $251 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Code Bugs in Security Architecture Kit, Source Code in DevOps, Infrastructure As Code in DevOps, Source Code Management Tools in DevOps.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the technical and coordination challenges of maintaining secure, reliable, and efficient DevOps workflows, comparable in scope to a multi-workshop program addressing real incidents in SAST integration, test debt governance, production debugging, IaC security, container supply chain controls, asynchronous system resilience, cross-language quality management, and pipeline performance diagnosis.

Module 1: Integrating Static Application Security Testing (SAST) into CI/CD Pipelines

  • Decide whether to fail builds on critical SAST findings or allow overrides with documented exceptions based on risk severity and remediation timelines.
  • Configure SAST tools to analyze only changed files in pull requests to reduce false positives and improve developer feedback speed.
  • Implement policy-as-code rules to standardize SAST thresholds across multiple repositories and enforce compliance during merge checks.
  • Balance scan depth versus pipeline duration by adjusting analysis scope—full project scans versus incremental—based on deployment frequency.
  • Integrate SAST results into developer dashboards and ticketing systems to ensure findings are tracked to resolution, not just reported.
  • Manage credential access for SAST tools in shared pipeline environments to prevent exposure while maintaining auditability.

Module 2: Managing Technical Debt in Automated Testing Frameworks

  • Establish ownership models for test maintenance when test suites span multiple teams with overlapping code ownership.
  • Refactor flaky UI tests into API or contract tests where possible to reduce execution time and environmental dependencies.
  • Decide when to quarantine failing tests versus fixing or deleting them based on historical failure rates and business impact.
  • Implement versioning for shared test libraries to prevent breaking changes across service teams during upgrades.
  • Allocate sprint capacity for test modernization by treating test debt with the same rigor as production code debt.
  • Measure and report test effectiveness using metrics like mutation score and defect escape rate, not just pass/fail counts.

Module 3: Debugging Distributed Systems in Production

  • Design trace context propagation across message queues and external APIs to maintain end-to-end visibility in microservices.
  • Configure sampling rates for distributed tracing to balance observability depth with storage cost and performance overhead.
  • Implement structured logging with consistent field naming to enable cross-service correlation during incident investigations.
  • Enforce log redaction rules at ingestion to prevent sensitive data exposure while preserving debug utility.
  • Use canary deployments with real-time error rate monitoring to isolate regressions before full rollout.
  • Define thresholds for automated log and metric alerts that minimize noise while capturing meaningful anomalies.

Module 4: Securing Infrastructure as Code (IaC) Workflows

  • Scan IaC templates for misconfigurations (e.g., public S3 buckets, open security groups) before provisioning resources.
  • Restrict who can approve and merge infrastructure changes based on environment criticality and change impact.
  • Enforce drift detection mechanisms to identify and remediate manual changes made outside of IaC pipelines.
  • Manage secrets used in IaC execution contexts through short-lived tokens and dynamic secret injection.
  • Version and test IaC modules independently to prevent breaking changes in shared environments.
  • Implement automated rollback procedures for failed infrastructure deployments using state comparison tools.

Module 5: Governing Container Image Supply Chains

  • Enforce base image approval policies to prevent use of unvetted or end-of-life container images.
  • Scan container images for known vulnerabilities and license compliance before promoting to production registries.
  • Require cryptographic signing of images using tools like Cosign to verify provenance in multi-team environments.
  • Implement image immutability in registries to prevent overwrites and ensure deployment consistency.
  • Define retention policies for container images based on usage, age, and security posture.
  • Monitor for runtime deviations from declared container capabilities (e.g., privilege escalation) using runtime security tools.

Module 6: Resolving Race Conditions in Asynchronous Workflows

  • Design idempotent job processors to handle duplicate messages from message brokers during retries or failovers.
  • Implement distributed locking strategies using Redis or database constraints to prevent concurrent access to shared resources.
  • Use versioned data records or optimistic concurrency control to detect and reject stale writes in high-throughput services.
  • Trace asynchronous job chains using correlation IDs passed through queues and event buses.
  • Configure retry backoff policies to avoid thundering herd problems during transient system outages.
  • Log state transitions of long-running workflows to reconstruct execution paths during debugging.

Module 7: Enforcing Code Quality Gates Across Polyglot Environments

  • Standardize code formatting and linting rules across multiple languages using centralized configuration repositories.
  • Integrate quality gate checks into pull request validation to block merges that degrade cyclomatic complexity or test coverage.
  • Adapt quality thresholds by language and project maturity to avoid imposing uniform standards on legacy systems.
  • Aggregate code quality metrics into executive dashboards with drill-down capability for team-level accountability.
  • Automate technical debt estimation using static analysis tools and map findings to business risk categories.
  • Coordinate cross-team alignment on acceptable tech stack variations to prevent uncontrolled language sprawl.

Module 8: Diagnosing Performance Regressions in Deployment Pipelines

  • Instrument pipeline stages with timing metrics to identify bottlenecks in build, test, and deployment phases.
  • Compare resource utilization (CPU, memory, I/O) across pipeline runners to detect misconfigured or overloaded agents.
  • Cache dependencies and build artifacts securely to reduce redundant downloads and compilation steps.
  • Isolate performance impacts from external dependencies like artifact registries or cloud APIs using synthetic monitoring.
  • Implement pipeline stage timeouts to prevent indefinite hangs and free up shared resources.
  • Rotate and archive pipeline logs to maintain query performance while preserving audit trails.
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