Release Planning cb-24: Automated defect prioritization using automation rules

ALM PTC Codebeamer
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1

I wanted to share our successful implementation of automated defect prioritization in codebeamer ALM cb-24 Release Planning module. Before automation, our team spent 4-5 hours weekly in manual triage meetings sorting through 200+ defects to determine which should be included in upcoming releases.

We built automation rules that analyze defect attributes (severity, customer impact, affected components) and automatically assign priority scores and suggested release targets. This has reduced our manual triage overhead by approximately 70% and improved the consistency of our prioritization decisions.

Happy to share details about our implementation approach and the specific automation rules we configured.

2

I’m interested in the component criticality aspect. How do you maintain that metadata? Is it a static configuration or does it adapt based on production incidents?

3

How did you implement the customer impact counting? Do you have a separate field where customers are linked to defects, or are you parsing something from the description? We track customer impact in comments which makes it hard to automate. Also curious if your rules integrate with release capacity planning - does it consider how many defects can realistically fit in a sprint?

4

Our scoring uses a weighted algorithm - Severity (40%), Customer Impact count (30%), Component criticality (20%), and Age (10%). Edge cases are handled by setting a confidence threshold - anything below 70% confidence goes to manual review queue. We also allow team leads to override automated priorities with a required justification comment that gets logged for tracking.

5

We created a custom multi-select reference field called “Affected Customers” that links to our customer database. This makes counting straightforward. For capacity planning integration, our automation rules check the target release’s remaining capacity before assigning defects - if a release is at 80% capacity, lower priority defects get pushed to the next release automatically.

6

We implemented something similar but struggled with keeping the rules up to date as priorities shifted. Do you version your automation rules? And how often do you review and adjust the scoring weights? Our initial weights became outdated within two months as business priorities changed.

7

Let me provide a detailed walkthrough of our automated defect prioritization implementation in cb-24 that you can adapt for your environments.

Architecture Overview:

Our system uses three layers of automation rules working together:

  1. Data enrichment rules that calculate component criticality scores
  2. Priority scoring rules that evaluate defect attributes
  3. Release assignment rules that match defects to appropriate releases based on capacity

Component Criticality Configuration:

We maintain a Component Master tracker with fields for each component including a calculated “Criticality Score” (1-10 scale). This score adapts dynamically based on:

  • Production incident count (last 90 days)
  • User base size affected by component
  • Regulatory/compliance requirements
  • Manual override by architecture team

An automation rule recalculates these scores weekly:


// Pseudocode - Component criticality calculation:
1. Query production incidents for component (last 90 days)
2. Calculate incident_weight = (critical_count * 10) + (high_count * 5)
3. Retrieve user_base size from component metadata
4. Set base_score = (incident_weight * 0.6) + (user_base/1000 * 0.3)
5. Apply compliance_multiplier if component is regulated
6. Update component.criticalityScore field

Priority Scoring Automation Rule:

When a defect is created or updated, this rule calculates the priority score:


// Trigger: Defect created or severity/impact changed
// Conditions: Status != Closed
// Actions:
1. Get severity_value (Critical=100, High=75, Medium=50, Low=25)
2. Count affected_customers from custom reference field
3. Get component.criticalityScore from linked component
4. Calculate age_factor = MIN(days_since_creation / 30, 1.0)
5. Calculate final_score =
   (severity_value * 0.4) +
   (affected_customers * 5 * 0.3) +
   (component.criticalityScore * 10 * 0.2) +
   (age_factor * 100 * 0.1)
6. Set defect.priorityScore = final_score
7. Set defect.confidence = calculated based on field completeness

Release Assignment Logic:

A separate automation rule runs nightly to assign defects to releases:


// Pseudocode - Release assignment:
1. Query all unassigned defects WHERE priorityScore >= 60 AND confidence >= 70
2. Get active releases ordered by planned_date ASC
3. For each release:
   a. Calculate remaining_capacity = planned_points - assigned_points
   b. If remaining_capacity > 20% of total:
      - Assign highest priority defects up to 80% capacity threshold
   c. Tag defects as "Auto-assigned" for review
4. Defects below confidence threshold → "Manual Review" queue
5. Send daily digest email to release managers with assignments

Edge Case Handling:

We built in several safety mechanisms:

Override Workflow: Team leads can manually change priority with required justification. The automation respects manual overrides and won’t recalculate unless explicitly reset.

Review Queue: Defects with confidence < 70% (missing customer impact, unclear severity) go to a dedicated review board for manual triage.

Capacity Guards: Releases never exceed 80% capacity through automation - the remaining 20% is reserved for manual additions and urgent fixes.

Validation Reports: Weekly dashboard shows automation accuracy - we track how often manual overrides occur and adjust weights quarterly based on patterns.

Implementation Steps:

  1. Phase 1 (Week 1-2): Set up custom fields (Affected Customers reference, Priority Score number, Confidence percentage, Component Criticality)

  2. Phase 2 (Week 3-4): Build and test data enrichment rules in a sandbox tracker with historical defects

  3. Phase 3 (Week 5-6): Implement priority scoring rules with conservative thresholds, run in “advisory mode” where scores are calculated but not acted upon

  4. Phase 4 (Week 7-8): Enable release assignment automation for one release as pilot, compare results against manual triage

  5. Phase 5 (Week 9+): Roll out to all releases, establish quarterly review cadence for weight adjustments

Maintenance and Tuning:

We version our automation rules in a configuration tracker and review quarterly:

  • Analyze override patterns to identify systematic scoring errors
  • Adjust weights based on business priority shifts (we’ve modified weights 3 times in 8 months)
  • Review component criticality scores monthly as production patterns change
  • Collect feedback from release managers on automation accuracy

Results After 8 Months:

  • Manual triage time reduced from 4-5 hours/week to 1-1.5 hours/week
  • Prioritization consistency improved (measured by inter-rater agreement among release managers)
  • Faster defect resolution - high-priority defects now assigned to releases within 24 hours vs 1-2 weeks previously
  • Better release predictability - fewer last-minute priority changes during sprint planning

Key Success Factors:

  • Start with conservative automation - don’t try to automate everything immediately
  • Maintain human oversight through confidence thresholds and review queues
  • Make the automation transparent - team members can see exactly how scores are calculated
  • Build in feedback loops so the system improves over time
  • Document the business logic clearly for audit and onboarding purposes

The cb-24 automation capabilities are robust enough to handle complex prioritization logic while maintaining the flexibility to adapt as your business needs evolve. The key is starting simple and iterating based on real usage patterns.