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Application Development in Problem Management

Application Development in Problem Management

$201.00
When you get access:
Course access is prepared after purchase and delivered via email
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.
How you learn:
Self-paced • Lifetime updates
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What does the Application Development in Problem Management course cover?

Application Development in Problem Management is covered here in 7 modules: Defining Problem Management Scope and Integration with Development Lifecycle, Problem Detection and Data Aggregation from Application Systems, Root Cause Analysis Techniques for Complex Application Failures and 4 more. The outline lists 42 specific topics, opening with determine whether problem management will be integrated into CI/CD pipelines or operate as a separate.

How do you approach Application Development in Problem Management step by step?

The work is sequenced in 7 stages. It starts with Defining Problem Management Scope and Integration with Development Lifecycle, moves through Problem Detection and Data Aggregation from Application Systems and Root Cause Analysis Techniques for Complex Application Failures, and ends at Metrics, Reporting, and Continuous Improvement. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Application Development in Problem Management course?

Module 1 is Defining Problem Management Scope and Integration with Development Lifecycle. It works through determine whether problem management will be integrated into CI/CD pipelines or operate as a separate post-deployment feedback loop based on organizational maturity and tooling constraints., select integration points between incident records and problem tickets to ensure development teams receive structured root cause data without duplicating effort across.

How is the Application Development in Problem Management course delivered?

The Application Development in Problem Management 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 Application Development in Problem Management course cost?

The Application Development in Problem Management course is $200 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: WebSphere Application Server Problem Determination Toolkit, Problem Determination in WebSphere Application Server, Problem Solving and Scrumban Scrum and Kanban team.

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

This curriculum spans the design and implementation of problem management practices across the application development lifecycle, comparable in scope to a multi-workshop technical advisory engagement focused on integrating development, operations, and service management workflows in complex, distributed systems.

Module 1: Defining Problem Management Scope and Integration with Development Lifecycle

  • Determine whether problem management will be integrated into CI/CD pipelines or operate as a separate post-deployment feedback loop based on organizational maturity and tooling constraints.
  • Select integration points between incident records and problem tickets to ensure development teams receive structured root cause data without duplicating effort across service management and DevOps tools.
  • Decide whether problem identification will be triggered manually by engineers or automated via anomaly detection in monitoring systems, weighing signal-to-noise ratio and alert fatigue.
  • Establish ownership boundaries between operations, SRE, and development teams for problem ticket creation and resolution to prevent accountability gaps.
  • Map problem records to specific application components or services in a microservices environment to enable targeted codebase investigations.
  • Define criteria for escalating recurring incidents to formal problem records, including frequency thresholds and business impact metrics.

Module 2: Problem Detection and Data Aggregation from Application Systems

  • Instrument application logging to include correlation IDs and error classification tags that enable automated clustering of similar failures across distributed services.
  • Configure log shippers and APM tools to forward structured exception data to problem management platforms without overwhelming downstream systems with volume.
  • Implement custom metrics to track error recurrence rates per endpoint or transaction type to identify patterns invisible in standard monitoring dashboards.
  • Design data retention policies for problem-related telemetry that balance forensic analysis needs with compliance and storage cost constraints.
  • Normalize error codes and exception types across polyglot services to enable consistent problem categorization and trend analysis.
  • Integrate synthetic transaction results with problem detection workflows to distinguish user-impacting issues from backend-only failures.

Module 3: Root Cause Analysis Techniques for Complex Application Failures

  • Apply fault tree analysis to distributed transaction failures by reconstructing call graphs from distributed tracing data to isolate contributing services.
  • Conduct blameless postmortems for production outages with participation from developers, QA, and infrastructure teams to uncover systemic gaps.
  • Use code blame and recent deployment data to correlate problem onset with specific commits, while avoiding premature attribution without runtime evidence.
  • Reproduce production-like failure conditions in staging environments using traffic replay tools, considering data privacy and infrastructure parity limitations.
  • Perform dependency chain analysis to determine whether a problem originates in application code, third-party libraries, or underlying platform behavior.
  • Document RCA findings in structured templates that link evidence, hypotheses, and validation steps to support auditability and knowledge reuse.

Module 4: Designing and Prioritizing Permanent Technical Remediations

  • Assess whether to refactor, patch, or decommission legacy code contributing to recurring problems based on technical debt and business continuity requirements.
  • Negotiate placement of remediation work in sprint backlogs by providing product owners with impact data tied to SLA breaches and user complaints.
  • Develop feature flags or circuit breakers as interim mitigations while permanent fixes undergo full testing and approval cycles.
  • Validate fix effectiveness through canary deployments that monitor error rate reduction in production subsets before full rollout.
  • Update automated test suites with regression tests derived from problem scenarios to prevent recurrence after deployment.
  • Coordinate cross-team remediation efforts when root cause spans multiple service boundaries, requiring shared timelines and integration testing.

Module 5: Change Implementation and Risk Control for Problem Fixes

  • Submit problem-related changes through formal change advisory board (CAB) processes when modifications affect high-availability systems or regulated components.
  • Define rollback procedures for problem fixes that introduce new dependencies or architectural changes, including data migration reversibility.
  • Enforce peer review requirements for problem resolution code, mandating at least one reviewer with domain knowledge of the affected module.
  • Integrate static analysis and security scanning into the fix deployment pipeline to prevent introducing new vulnerabilities during remediation.
  • Time deployments of critical fixes outside of peak user activity windows, coordinating with operations and customer support teams.
  • Document configuration changes associated with problem resolution in configuration management databases (CMDB) for audit and troubleshooting purposes.

Module 6: Knowledge Management and Feedback Loop Closure

  • Convert validated problem resolutions into runbook entries for operations teams, specifying detection signals and automated response actions.
  • Populate internal knowledge bases with developer-focused summaries that explain root causes and code-level implications of resolved problems.
  • Link resolved problem tickets to relevant documentation updates, such as API behavior changes or deployment prerequisites.
  • Archive problem records with metadata indicating resolution type (code fix, config change, design deprecation) to support future trend analysis.
  • Conduct periodic reviews of open problem tickets to validate continued relevance and prevent stale issues from accumulating.
  • Integrate problem resolution outcomes into developer onboarding materials to communicate historical failure patterns and design constraints.

Module 7: Metrics, Reporting, and Continuous Improvement

  • Track mean time to problem resolution (MTTPR) across application tiers to identify bottlenecks in diagnosis or fix deployment processes.
  • Measure the percentage of recurring incidents that reoccur after problem closure to assess remediation effectiveness and testing coverage.
  • Generate heatmaps of problem density by code module or team to guide refactoring investments and resource allocation.
  • Report on the ratio of proactive problem identification versus reactive post-incident analysis to evaluate maturity of detection mechanisms.
  • Use problem backlog aging reports to escalate long-standing issues requiring architectural investment or executive sponsorship.
  • Calibrate problem management KPIs quarterly with engineering leadership to ensure alignment with evolving system complexity and business objectives.
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