There is no single way to evaluate developers — there are approaches, and each one optimizes for something different. Before adopting a model, it is worth understanding what each family encourages in practice, because every metric shapes behavior: a team delivers whatever the model rewards.
Forced ranking
Stack ranking compares people against each other and spreads the team along a curve. The problem is structural: even when the whole team improves, someone is still last. The model encourages internal competition, discourages collaboration and penalizes strong teams — being average on an excellent team counts for less than standing out on a weak one.
Volume metrics
Counting commits, lines of code or hours as a direct measure of productivity runs into Goodhart's law: when a metric becomes a target, it stops being a good metric. Volume is easy to inflate — sliced-up commits, redundant code, hours logged with no criteria — and the model ends up rewarding motion instead of value.
System frameworks
Approaches such as DORA and SPACE measure the delivery system: lead time, deployment frequency, change failure rate, team satisfaction. They are excellent for diagnosing organizational bottlenecks — but they were designed not to evaluate individuals. They do not answer the conversations management needs to have: 1:1s, promotions, career development.
Perception-based evaluation
360 reviews and performance forms bring rich human context, but they suffer from well-known biases: recency (the last month outweighs the whole cycle), proximity (the most visible people get the best reviews) and halo (one strength colors the read on everything else). On its own, perception turns evaluation into a popularity contest.
Absolute scoring with reference bands
The approach Devint takes combines the previous ones while fixing their flaws: automatic signals from the real workflow, structured leadership reviews and comparison against healthy bands — not against peers. When the whole team improves, the improvement shows up as collective. And the saturation ceiling defuses gaming: above the band, extra volume does not raise the score.
How to choose a model
The decisive question is not "which model is most accurate?", but "what behavior does this model encourage?". A good test:
- If the whole team improves together, does the model show it — or does it manufacture a last place?
- Can you improve the result by inflating volume — or is there a ceiling?
- Does the model support individual conversations (1:1s, promotions) — or only system-level diagnosis?
- Does the read combine objective evidence with human judgment — or does it rely on only one of the two?
For a detailed look at the approach Devint takes — dimensions, weights and bands — see the full methodology.