Debugging a Failed Metrics Initiative
Modern software organizations love key performance indicators (KPIs). From DevOps DORA metrics (deployment frequency, lead time, etc.) to agile velocity and defect counts, leaders use numbers to gauge productivity and quality. The intent is sound; metrics provide objective feedback and clear targets. But under pressure to “hit the numbers,” even good metrics can turn counterproductive. This phenomenon is so common that social scientists coined laws for it.
Goodhart’s Law warns: “When a measure becomes a target, it ceases to be a good measure.” In other words, once people are rewarded on a metric, they often game it, improving the number without improving the underlying outcome. Campbell’s Law adds that the more a quantitative metric is used for decision-making, the more it will be corrupted and distort the very process it’s intended to monitor.
In software teams, this means a well-intended KPI can create perverse incentives and dysfunctional behavior if it’s pursued as an objective in itself.
Management scholar Robert Austin noted that measuring performance with simplistic metrics often changes the behavior being measured. Deming famously warned that people forced to meet numeric goals will do so “even if they have to destroy the enterprise to do it.”.
Common Engineering Metrics Anti-Patterns
Software engineering is full of examples where metrics induce unintended behavior. Here are a few prevalent KPI anti-patterns and their side effects:
| Well-Intentioned Metric Target | Distorted Behavior and Consequences |
|---|---|
| Bug Count as a Performance Metric (Testers rewarded by bugs found) |
Testers prioritize quantity over severity, logging many trivial or duplicate bugs to boost their numbers. The bug count increases, but product quality doesn’t improve and velocity deteriorates. |
| Pull Requests per Developer (Quotas for code merged) |
Developers break work into many small PRs to meet quotas. This inflates activity but wastes review time and reduces focus on meaningful work. GitHub and Microsoft Research caution that raw activity metrics are misleading. |
| Sprint Velocity (Teams pushed to increase velocity) |
Teams inflate point estimates or prioritize low-effort stories. Velocity becomes a vanity metric disconnected from customer impact. |
| Deployment Frequency Goals (e.g. “X releases per day”) |
Teams deploy trivial or no-op updates to meet targets. DORA warns that arbitrary frequency targets encourage gaming rather than improved performance. |
Each of these metrics is valuable in context. The problem arises when metrics are misused as quotas or incentives, separated from their intended purpose.
Signs Your Metrics Are Driving the Wrong Behavior
Engineering leaders should watch for these red flags:
- Suddenly perfect numbers – defect counts fall to zero, or every developer hits 100% of their quota. This often means the data is being manipulated to meet expectations.
- Local wins, global losses – velocity is up, but cycle time, incident volume, or customer satisfaction are stagnant.
- Cynical commentary – engineers joke about gaming the system.
- Flatline metrics – data consistently hovers at or near a target value with no variance, suggesting synthetic compliance.
When these signals appear, the issue is rarely the metric itself. It's the way the metric is being enforced or incentivized.
Rebalancing Metrics for Better Insight
Here are pragmatic ways to turn metrics into tools for improvement instead of traps for distortion.
Restore Purpose and Context
Tie each metric to a real goal. For example, don’t measure bug count in isolation. Track escaped defect rate, severity, and customer impact instead. Metrics should illuminate a system, not serve as ends in themselves.
Use Metrics in Balanced Sets
Pairs like deployment frequency and change failure rate help prevent gaming. The DORA model and SPACE framework both advocate using sets of metrics across speed, quality, collaboration, and satisfaction to maintain a balanced view (Forsgren et al., 2021).
Prefer Team Metrics over Individual Metrics
Avoid measuring individual outputs like commit counts. Measure team-level Throughput or Flow efficiency instead. This reinforces shared accountability and discourages local optimization.
Use Metrics as Inputs for Learning
Metrics should start conversations, not end them. If review time spikes, ask why. If sprint throughput drops, explore blockers. Separate metrics from punitive consequences or rewards to encourage transparency.
Evolve Your Metrics Over Time
Retire metrics that are no longer useful or that teams have learned to game. Invite feedback from the people using the metrics. A responsive measurement system builds trust.
Reward Qualitative Signals
Not everything that matters can be quantified. Ask teams to share insights from postmortems or improvement experiments. Recognize efforts that improve architecture, knowledge sharing, or onboarding quality even if those efforts don’t show up in the dashboard.
Reframing Metrics as a Leadership Tool
Well-designed metrics support high-performance engineering cultures. They provide evidence to reduce bias, identify risk, and track improvement. But they must be applied thoughtfully.
Avoid turning metrics into targets. Use them to inform decisions and identify bottlenecks. Combine quantitative signals with qualitative understanding. Treat every metric as a prompt to ask better questions.
In doing so, you protect the trust, motivation, and engineering judgment that no dashboard can replace. And that’s when KPIs start serving leadership.