Hey everyone, Alex here. Welcome back to another edition of Coding with Alex on sysseder.com.
If you've been scanning the tech and education headlines this week, you probably saw a major announcement that triggered some heavy debates: The University of Michigan is dropping first-semester grades for incoming freshmen, transitioning instead to a "pass/fail" (or "no record") system to curb an escalating mental health crisis among students. The theory is simple: remove the high-stakes, hyper-competitive metric early on to allow students to focus on deep learning, adaptation, and systemic acclimation without the crushing anxiety of a GPA drop.
Now, you might be asking: "Alex, this is a software engineering and DevOps blog. Why are we talking about college grading policies?"
Because as developers, engineering managers, and site reliability engineers (SREs), we are living in our own high-stakes grading system every single day.
We are constantly measured by metrics: Lines of Code (LoC), pull request throughput, deployment velocity, cycle time, story points completed, uptime decimals (the elusive "five nines"), and mean time to resolution (MTTR). And just like those stressed-out college freshmen, our industry is facing a quiet, devastating burnout epidemic. Let's dive deep into why bad metrics ruin engineering cultures, how we can apply systemic "grade-masking" to our deployment pipelines, and how to build telemetry that measures system health without destroying human health.
The Fallacy of the Proxy Metric (Why LOC and Story Points Fail)
In education, a letter grade is a "proxy metric." It is an imperfect, highly compressed representation of a student's actual comprehension, critical thinking, and effort. Over time, students learn to optimize for the metric (memorizing for the exam) rather than the actual goal (retaining knowledge). This is Goodhart’s Law in action: "When a measure becomes a target, it ceases to be a good measure."
In software development, we fall into this trap constantly. When management demands higher velocity, developers optimize for the metrics being tracked. Let's look at a classic anti-pattern: tracking Lines of Code (LoC) or PR Count.
The "Code Volume" Trap
If an engineering team is graded on how many lines of code they commit, they will naturally write verbose, redundant, and highly unoptimized code. Consider these two JavaScript implementations of a utility that flattens a nested array:
// Implementation A: Highly verbose, optimized for "Lines of Code" metrics
function flattenArrayVerbose(arr) {
const result = [];
for (let i = 0; i < arr.length; i++) {
if (Array.isArray(arr[i])) {
const temp = flattenArrayVerbose(arr[i]);
for (let j = 0; j < temp.length; j++) {
result.push(temp[j]);
}
} else {
result.push(arr[i]);
}
}
return result;
}
// Implementation B: Modern, idiomatic, optimized for readability and simplicity
const flattenArrayClean = arr => arr.flat(Infinity);
Under a metric system that rewards code volume, the developer who wrote Implementation A looks like a superstar. The developer who wrote Implementation B (which uses the native ECMAScript flat() method) looks like they barely worked. Yet, Implementation B is significantly easier to maintain, less prone to off-by-one errors, and highly readable.
DORA Metrics Done Right: Measuring Systems, Not People
If we shouldn't measure developers by individual output metrics, what should we measure? This is where the DevOps Research and Assessment (DORA) metrics come in. The key distinction of DORA metrics is that they measure team and system outcomes rather than individual developer performance.
Let's look at the four core DORA metrics and how we can track them programmatically without creating a panopticon that burns out our engineers:
- Deployment Frequency: How often does your team successfully deploy to production?
- Lead Time for Changes: How long does it take for a commit to go from code check-in to running in production?
- Change Failure Rate: What percentage of deployments cause a failure in production requiring immediate rollback or hotfixing?
- Failed Service Recovery Time (MTTR): How long does it take to restore service when a production outage occurs?
Implementing a Automated DORA Tracker (The Safe Way)
Instead of manual logging or performance-review-linked tracking, DORA metrics should be collected passively via your CI/CD pipelines and incident response systems. Here is a conceptual architecture of how to pipeline this data safely using GitHub Actions and a lightweight telemetry receiver:
+------------------+ +-------------------+ +---------------------+ | GitHub Actions | ---> | Telemetry API | ---> | PostgreSQL Database | | (Workflow Run) | | (Anonymized ops) | | (Aggregated Trends) | +------------------+ +-------------------+ +---------------------+
Here is a snippet of a GitHub Actions workflow that automatically logs deployment telemetry to an internal analytical database upon a successful release. Notice that we do not send the individual committer's identity. We only care about the system event!
name: Production Deployment Telemetry
on:
release:
types: [published]
jobs:
telemetry:
runs-on: ubuntu-latest
steps:
- name: Send Deployment Event to Telemetry DB
run: |
curl -X POST https://telemetry-api.internal.sysseder.com/v1/deployments \
-H "Authorization: Bearer ${{ secrets.TELEMETRY_API_KEY }}" \
-H "Content-Type: application/json" \
-d '{
"repository": "${{ github.repository }}",
"environment": "production",
"status": "success",
"run_id": "${{ github.run_id }}",
"timestamp": "'$(date -u +"%Y-%m-%dT%H:%M:%SZ")'"
}'
By capturing telemetry at the repository level without tying it to an individual developer's name, we focus on the team's shipping pipeline. If the lead time is too long, it's not because "Developer X is slow"—it's because our CI suite is taking 45 minutes to run, or our deployment staging environment is flaky. This shifts the conversation from blame to systemic optimization.
The SRE Equivalent of "Pass/Fail": Error Budgets
The University of Michigan's move to drop first-semester grades is fundamentally about establishing a safety margin—a period where mistakes do not equal permanent, catastrophic failure.
In Site Reliability Engineering (SRE), we have a brilliant, mathematically defined equivalent to this safety margin: Error Budgets.
An Error Budget is the allowable threshold of unreliability your system can tolerate before it impacts your users significantly. It is derived directly from your Service Level Objectives (SLOs). For example, if you target a 99.9% uptime (SLO) over a 30-day period, your system has an error budget of 0.1%.
The Formula for Error Budget
$$\text{Error Budget} = 100\% - \text{SLO}$$
If your service receives 10,000,000 requests per month, a 99.9% SLO allows for 10,000 failed requests.
How Error Budgets Protect Developer Mental Health
Without an explicit error budget, engineering cultures default to an impossible target: 100% uptime. This creates extreme deployment anxiety, finger-pointing, and operational paralysis.
When you implement an error budget, you are telling your developers: "You are allowed to fail up to this limit." If you have plenty of error budget left, developers should be encouraged to take risks, deploy new features, and experiment. If the error budget is depleted (e.g., due to a bad release or an upstream dependency failure), the "fail-safe" kicks in: feature deployments are paused, and the team shifts focus entirely to reliability, automated testing, and infrastructure stability.
It's the ultimate operational "Pass/Fail" system. It removes the daily dread of perfection and replaces it with shared, objective system boundaries.
Actionable Steps: Building a Healthy Engineering Culture
If you are a lead developer, an engineering manager, or an architect, you have the power to steer your team's culture away from toxic metrics. Here is how you can start today:
- Audit Your Dashboard: Look at your team's internal Jira or Grafana dashboards. If you see charts displaying "Story Points Completed per Developer" or "Commits per Developer," delete them immediately. Replace them with cycle time, change failure rate, and application performance metrics.
- Normalize the "Blameless Post-Mortem": When a production issue occurs, never ask who did it. Ask what in the system allowed the mistake to bypass production safeguards. (e.g., "Why didn't our staging integration tests catch this null pointer exception?").
- Implement "No-Production-Deploy Fridays" (or Keep Them Safe): If your deployment pipeline is highly manual and stressful, give your developers a cognitive break. Reserve Fridays for technical debt cleanups, documentation, and localized experimentation rather than high-stakes production rollouts.
Wrapping Up
The University of Michigan's decision to drop first-semester grades is a reminder that humans are not machines. When the pressure to perform perfectly under a broken measurement system becomes too great, the system breaks.
The next time you review a pull request, debug a failing unit test, or look at a team sprint velocity chart, ask yourself: Are we measuring what actually matters, or are we just optimizing for the grade? Let's build robust pipelines, establish clear error budgets, and cultivate environments where failing forward is part of the architecture, not a career ender.
What about you? Does your team use metrics that you feel are counter-productive, or have you successfully implemented DORA metrics in a healthy way? Let's chat in the comments below!
Until next time, happy coding.