sdlc-post-deployment/SKILL.md
Generate a Post-Deployment Evaluation Report (PDER) to assess software health after production deployment. Grounded in ISO/IEC/IEEE 14764:2022 Clause 6 mandatory outcomes and Grubb & Takang's operational metrics. Covers system availability...
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sdlc-post-deployment or would be better handled by a more specific companion skill.SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.Generate a Post-Deployment Evaluation Report (PDER) that assesses software health after a production release. This skill produces 1 primary document grounded in ISO/IEC/IEEE 14764:2022 mandatory process outcomes and Grubb & Takang (2003) first-year operational metrics.
world-class-engineering.observability-monitoring, reliability-engineering, and deployment-release-engineering.The PDER must connect:
sdlc-testing skill (Test Completion Report covers this)sdlc-user-deploy skillsdlc-maintenance skill (PDER feeds into it; it does not replace it)sdlc-design skill| # | Document | Purpose | Audience | Phase | |---|----------|---------|----------|-------| | 1 | Post-Deployment Evaluation Report (PDER) | Evidence-based assessment of system health: operational metrics, maintenance type mix, lessons learned, recommended actions | PM, stakeholders, CCB, maintenance team | 30–90 days post-release |
The PDER satisfies the five mandatory outcomes of ISO/IEC/IEEE 14764:2022 §6.4.13.2 (maintenance process outcomes). These outcomes are non-optional: a deployment that cannot produce evidence for all five has an incomplete maintenance process.
Relationship to Osborne's Model: Grubb & Takang (2003) describe Osborne's post-installation review as the formal process gate that triggers PDER creation. The review is scheduled at the end of the initial operational period (30–90 days) and produces structured findings that transition the system from the delivery phase into the formal maintenance phase.
The PDER must provide evidence for each outcome. Flag [OUTCOME-UNMET] for any outcome where evidence cannot be gathered.
| Outcome | Description | PDER Section That Addresses It | |---------|------------|-------------------------------| | a) | Maintenance constraints identified | Operational Health Metrics + Documentation Currency | | b) | Enabling systems and services available | Deployment Summary + Environment Status | | c) | Replacement, repaired, or revised elements available | Maintenance Type Mix Actuals | | d) | Need for corrective, perfective, and adaptive changes reported | Change Request Analysis + Recommended Actions | | e) | Failure and lifetime data (including costs) determined | MTTR Breakdown + Cost Data |
Document identifier, system name, version under review, deployment date, reporting period (start and end dates), and the name of the evaluator or evaluation team.
Brief description of what was deployed: features delivered, known issues at go-live, deviations from the release plan, and the environment the system is running in (OS, database version, hosting platform). Cross-reference the Deployment Guide and Release Notes from sdlc-user-deploy.
These metrics are the quantitative core of the PDER. All metrics must be populated from actual operational data — no estimates.
Change Requests per KLOC (first year) The primary quality indicator per Grubb & Takang (2003). Measures the rate at which defects and change requests are filed against the delivered code base.
$$CR_{rate} = \frac{\text{Total MRs/PRs filed}}{\text{KLOC delivered}}$$
Flag if $CR_{rate}$ exceeds the project's planned threshold. If no project threshold exists, use the Grubb & Takang industry median as a reference benchmark.
Post-Operational Fault Count Total defects identified during the reporting period. Weight by severity:
$$Weighted_Faults = \sum (Severity_Weight_i \times Fault_Count_i)$$
where $Severity_Weight$ is defined in the project's STP (e.g., Critical=4, High=3, Medium=2, Low=1).
MTTR Breakdown Mean Time to Repair, decomposed into the six administrative phases. Each phase must be measured separately to identify process bottlenecks.
| Phase | Description | |-------|------------| | Recognition | Time from fault occurrence to detection/reporting | | Administrative | Time to assign, triage, and enter the MR/PR system | | Tools | Time waiting for environment access, build, or deploy tools | | Analysis | Time to diagnose root cause | | Specification | Time to design and specify the fix | | Change | Time to implement, test, and release the fix |
$$MTTR_{total} = T_{recognition} + T_{admin} + T_{tools} + T_{analysis} + T_{spec} + T_{change}$$
System Availability
$$Availability = \frac{Total_Hours - Downtime_Hours}{Total_Hours} \times 100%$$
Compare against the NFR-AVAIL target from the SRS.
Schedule Variance
$$SV = \frac{Planned_Hours - Actual_Hours}{Planned_Hours} \times 100%$$
A negative SV indicates overrun. Document root cause for any SV beyond ±10%.
Record the count and percentage of MRs/PRs by maintenance type for the reporting period. Compare to the planned distribution from the SMP (or the Lientz/Swanson baseline if no SMP exists).
| Type | Planned % | Actual Count | Actual % | Variance | |------|-----------|-------------|---------|---------| | Corrective | 20% | — | — | — | | Adaptive | 25% | — | — | — | | Perfective | 50% | — | — | — | | Preventive | 5% | — | — | — | | Additive | 0% | — | — | — |
Significant variance from the planned mix (>10 percentage points for any type) must be explained and fed back into the next SMP revision.
Summarize user satisfaction data gathered from surveys, support tickets, or feedback sessions. Caveat (mandatory): User satisfaction is an insufficient quality indicator on its own. Users adapt to poor software over time (Grubb & Takang, 2003). Pair satisfaction data with the structural metrics in Section 3 before drawing conclusions. High satisfaction combined with high $CR_{rate}$ or poor MTTR indicates a quality risk, not a clean bill of health.
List each project document and its currency status: current, needs update, or out of date.
| Document | Status | Last Updated | Action Required | |----------|--------|-------------|----------------| | Software Requirements Specification | — | — | — | | Software Design Document | — | — | — | | Software Test Plan | — | — | — | | Deployment Guide | — | — | — | | User Manual | — | — | — | | Software Maintenance Plan | — | — | — |
Documents marked "out of date" must be updated before the SMP maintenance cycle begins.
Structured findings from the deployment and first operational period. Each lesson must state: what happened, why it happened (root cause), and what should change. Lessons feed directly into SMP §9.1.8 (process improvements) and future project planning.
Prioritized action list: immediate corrective actions, adaptive changes required (e.g., upcoming OS upgrade), perfective improvements requested, and preventive measures identified. Each action must have an owner and a target date.
Based on the actual type mix observed and the lessons learned, recommend the maintenance effort distribution for the next period. Express as person-months per type. This recommendation feeds directly into SMP §9.1.10 (maintenance cost estimates).
Osborne's Model (Grubb & Takang, 2003) defines a formal post-installation review as the explicit process gate that transitions a system from the delivery phase into the maintenance phase. The review is mandatory — a system that bypasses this gate has no validated baseline for maintenance planning.
Trigger: End of the 30–90 day initial operational period following go-live. Participants: Maintenance Manager, a CCB representative, a user representative, and the project PM. Output: The signed PDER, which formally opens the maintenance phase and authorizes the SMP to become operative.
[OUTCOME-UNMET] flags raised for any ISO 14764 outcome lacking evidence| Anti-Pattern | Why It Fails | Do This Instead | |-------------|-------------|-----------------| | Running PDER at day 7 post-launch | Insufficient operational data; metrics are noise, not signal | Wait 30–90 days for a statistically meaningful sample | | Using user satisfaction as the only metric | Users adapt to poor software; satisfaction hides quality debt | Pair satisfaction with $CR_{rate}$, MTTR, and fault count | | Reporting total MTTR without phase breakdown | Cannot identify whether the bottleneck is in analysis, tools, or process | Decompose MTTR into all six phases; target the dominant phase | | Skipping Osborne's review gate | No formal transition from delivery to maintenance; SMP has no activation point | Schedule the review at day 30–90; sign the PDER to activate the SMP | | Treating the PDER as a pass/fail report | Stakeholders receive a summary without actionable detail | Every finding must link to a recommended action with an owner and date | | Not feeding PDER data back into the SMP | SMP cost estimates remain theoretical; next budget cycle repeats the same errors | Update SMP §9.1.10 using actual person-months from the PDER |
| Skill | Relationship |
|-------|-------------|
| sdlc-user-deploy | Deployment Guide and Release Notes provide the deployment context for PDER Section 2. |
| sdlc-testing | Test Completion Report provides the pre-release quality baseline against which post-release metrics are compared. |
| Skill | Relationship |
|-------|-------------|
| sdlc-maintenance | PDER is the primary input to the SMP. Cost actuals, type mix, and MTTR data seed SMP §9.1.10 estimates. PDER sign-off activates the SMP. |
| Skill | Phase | Status |
|-------|-------|--------|
| sdlc-planning | Planning & Management | Available |
| sdlc-design | Design & Architecture | Available |
| sdlc-testing | Testing & Quality | Available |
| sdlc-user-deploy | Delivery & Deployment | Available |
| sdlc-post-deployment | Post-Deployment Evaluation | This Skill |
| sdlc-maintenance | Software Maintenance | Available |
Back to: Skills Repository Related: sdlc-maintenance | sdlc-testing | sdlc-user-deploy Last Updated: 2026-03-15 (created per ISO/IEC/IEEE 14764:2022 Clause 6 + Grubb & Takang 2003)
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