You've decided to automate code maintenance. Good decision. Now what? Where do you start? What do you automate first? How do you roll out to the team without disrupting productivity?
Getting started with AI-powered maintenance doesn't require a massive transformation. Start small, prove value, and expand. This guide walks you through a practical path from zero to effective automated maintenance.
Before You Start
Prepare for success.
Assess Your Current State
Understand where you are:
Current state questions:
- How is maintenance done now?
- What's taking the most time?
- What's causing the most pain?
- What's the biggest risk?
Understanding current state guides priorities.
Define Success Criteria
Know what success looks like:
Success metrics to consider:
- Time saved on maintenance
- Issues caught before production
- Developer satisfaction
- Code health trends
Clear criteria enable measurement.
Get Stakeholder Buy-In
Ensure support:
Stakeholders to align:
- Engineering leadership
- Team leads
- Developers who'll use it
- Security/compliance if relevant
Buy-in enables adoption.
Phase 1: Start with Detection
Don't automate fixes first - start with visibility.
Enable Scanning
Get visibility into code health:
@devonair initial setup:
- Connect to repositories
- Enable code scanning
- Run first analysis
Scanning shows what exists.
Review Findings
Understand what was found:
@devonair review results:
- What issues were detected?
- Which are most critical?
- Which are most common?
- What's the overall picture?
Findings inform priorities.
Establish Baseline
Document starting point:
@devonair baseline metrics:
- Total issues by category
- Issue distribution by severity
- Code health score
- Key metrics to track
Baseline enables progress measurement.
Don't Fix Everything Yet
Resist the urge to fix everything:
First phase focus:
- Visibility, not remediation
- Understanding, not action
- Learning, not transforming
Understanding comes before action.
Phase 2: Automate Low-Risk Fixes
Start with safe automation.
Identify Safe Automations
Choose low-risk starting points:
@devonair safe starting points:
- Formatting fixes
- Simple lint violations
- Unused import removal
- Minor dependency updates
Low-risk wins build confidence.
Configure Automated PRs
Enable automatic fix creation:
@devonair configure auto-PR:
- Small, focused changes
- Clear descriptions
- Automatic testing
- Easy to review
Automated PRs start the flow.
Review and Merge
Human approval for automated changes:
@devonair review workflow:
- Review automated PRs
- Verify correctness
- Approve or reject
- Provide feedback
Human oversight builds trust.
Monitor Results
Track how it's going:
@devonair monitor early results:
- PRs created vs merged
- Issues found vs fixed
- Team feedback
- Any problems?
Early monitoring catches issues.
Phase 3: Expand Scope
As confidence builds, expand.
Add More Fix Types
Expand what's automated:
@devonair expand automation:
- More lint fixes
- Simple code improvements
- Test additions
- Documentation updates
Gradual expansion builds capability.
Add More Repositories
Cover more of your codebase:
@devonair expand coverage:
- Start: 1-2 pilot repositories
- Next: Team's other repositories
- Then: Adjacent team repositories
- Finally: Organization-wide
Gradual rollout manages risk.
Increase Automation Level
Reduce manual steps:
@devonair increase automation:
- Auto-merge for proven categories
- Faster processing
- Less human intervention needed
Proven automation earns autonomy.
Phase 4: Integrate into Workflow
Make maintenance part of normal work.
PR Integration
Feedback on every PR:
@devonair PR integration:
- Quality checks on every PR
- Suggestions before merge
- Standards enforcement
PR integration makes quality continuous.
CI/CD Integration
Maintenance in the pipeline:
@devonair CI/CD integration:
- Quality gates in CI
- Automated maintenance in pipeline
- Deployment verification
Pipeline integration automates flow.
Notification Integration
Keep team informed:
@devonair notification setup:
- Slack notifications for important findings
- PR comments for context
- Weekly summary reports
Notifications drive awareness.
Phase 5: Optimize and Scale
Refine for your context.
Tune Configuration
Optimize for your needs:
@devonair tune configuration:
- Adjust detection rules
- Configure fix preferences
- Set priority thresholds
Configuration optimization improves fit.
Build Team Habits
Establish maintenance culture:
@devonair build habits:
- Regular review of findings
- Quick response to PRs
- Feedback on automation
Habits make maintenance sustainable.
Measure and Improve
Track progress, improve continuously:
@devonair measure and improve:
- Track metrics over time
- Identify improvement opportunities
- Iterate on configuration
Continuous improvement maximizes value.
Common Challenges and Solutions
Anticipate and address challenges.
"Too Many Findings"
When initial scan is overwhelming:
Solution:
- Prioritize by severity
- Focus on active code first
- Address incrementally
- Don't try to fix everything at once
Prioritization handles volume.
"Team Resistance"
When developers push back:
Solution:
- Start with willing teams
- Show value before mandating
- Address specific concerns
- Make it help, not hinder
Value demonstrations overcome resistance.
"False Positives"
When automation suggests wrong things:
Solution:
- Configure exceptions
- Provide feedback
- Tune detection rules
- Accept some noise
Configuration reduces false positives.
"Integration Issues"
When it doesn't fit your workflow:
Solution:
- Start with minimal integration
- Add integrations gradually
- Customize for your workflow
- Get support when needed
Incremental integration reduces friction.
Timeline Expectations
What to expect when.
Week 1-2: Setup and First Scan
Early milestones:
- Connected to repositories
- First scan completed
- Findings reviewed
- Baseline established
Initial visibility achieved.
Week 3-4: First Automated Fixes
Early wins:
- Low-risk automation enabled
- First automated PRs created
- First fixes merged
- Process established
Value starts flowing.
Month 2-3: Expanded Coverage
Growth phase:
- More fix types enabled
- More repositories covered
- Team adoption growing
- Measurable improvements
Impact becomes visible.
Month 4+: Optimized Operation
Mature operation:
- Full automation where appropriate
- Organization-wide coverage
- Continuous improvement
- Strong ROI demonstrated
Full value realized.
Success Factors
What makes implementations succeed.
Start Small
Don't try to do everything:
Start small approach:
- One team
- Few fix types
- Limited scope
- Learn and expand
Small starts enable learning.
Show Value Early
Quick wins build support:
Early value:
- Fix real problems
- Save real time
- Prevent real incidents
- Show real metrics
Value builds momentum.
Involve the Team
Developers must be partners:
Team involvement:
- Get input on configuration
- Respond to feedback
- Celebrate successes
- Address concerns
Team ownership enables adoption.
Be Patient
Transformation takes time:
Patience required:
- Culture change is gradual
- Trust builds over time
- Full value takes months
- Persistent effort pays off
Patience enables success.
Getting Started Today
Take the first step.
Connect your code:
@devonair connect:
- Connect first repository
- Run initial scan
- Review findings
Enable first automation:
@devonair enable:
- Choose low-risk fix type
- Configure automation
- Create first PR
Build from there:
@devonair expand:
- Review results
- Add capabilities
- Grow coverage
Getting started with AI-powered maintenance isn't complicated. Start with visibility, add safe automation, expand gradually, and measure as you go. Each step builds on the previous one, and each success creates momentum for the next improvement.
FAQ
How long does setup take?
Initial connection and first scan typically take less than an hour. First automated fixes can happen the same day. Full rollout across an organization takes weeks to months depending on size.
Do we need to change our workflow?
Not significantly. AI maintenance integrates into existing workflows - PRs, CI/CD, communication channels. It enhances rather than replaces existing processes.
What if our code quality is really bad?
Actually, that's when automation helps most. Start with visibility, prioritize ruthlessly, and improve incrementally. Don't try to fix everything at once - make progress over time.
Should we start with the worst repository or the best?
Start with a moderate one - active enough to provide value, healthy enough that you can see success. Worst repositories can come later when you have experience.