Weave vs Swarmia | #1 in Engineering Metrics
The Standard Approach: Measuring the Process
To understand Weave vs. Swarmia it’s important to understand the timeline of engineering metrics. For years, the gold standard for engineering analytics has been a set of research-backed, process-centric metrics:
DORA Metrics: These four metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Time to Recovery) are widely used for understanding the health of your DevOps pipeline.
SPACE Framework: This framework adds more dimensions, looking at Satisfaction, Performance, Activity, Communication, and Efficiency to give a broader view of team health.
Platforms built around these frameworks are great at tracking the flow of work. They pull data from your Git provider and project management tools to surface insights on things like PR cycle time, review rate, and work-in-progress (WIP) limits.
This approach has real benefits. It helps you spot bottlenecks, establish team-wide working agreements, and integrate notifications into tools like Slack to keep everyone aligned. For many organizations, adopting these metrics fosters a cultural shift toward making evidence-based decisions, which can lead to improvements in how much time is spent on high-impact work. If you are looking for this then Swarmia does really well.
The problem is that these metrics primarily measure the container, not the contents. They tell you how fast your delivery pipeline is moving, but they can't tell you much about the substance or complexity of the work flowing through it.
The Missing Piece: Understanding the Work Itself
Here’s where the process-centric approach falls short. Imagine two pull requests:
PR #1: A one-line change to fix a typo in the UI.
PR #2: A 500-line refactor of a critical payment service.
Both PRs might have the exact same cycle time. From a process perspective, they look identical. But they represent vastly different amounts of effort, complexity, and value.
To get around this, many platforms supplement their system data with qualitative developer feedback from surveys. This helps add context, but it's not a perfect solution. Surveys are subjective, they only happen periodically, and let's be honest, they can feel like extra work for your team. Some leaders find they need tools that can dig deeper into the development process to get more actionable insights.
A Better Way: AI-Powered Output Analysis
What if you could objectively measure the work itself? That’s the next evolution in engineering metrics, and it’s powered by AI. Instead of just looking at metadata, this new approach uses LLMs and domain-specific machine learning to analyze the code and conversations within every pull request.
This is exactly what Weave built.
Here’s what an output-centric approach unlocks:
Objective Output Measurement: Don't just time PRs; quantify them. Weave analyzes the code changes to determine the complexity and scope of the work, telling you how long it would take an expert engineer to complete it. Now you can compare a typo fix and a major refactor on a level playing field.
Code Review Quality: Weave's AI scans every review comment to quantify its depth and quality, so you can see if your team is having meaningful discussions that improve code quality or just clicking "Approve."
Automated Investment Tracking: Manually categorizing work into buckets like "New Feature," "Tech Debt," or "Bug Fix" is tedious and often inaccurate. Our platform analyzes the work itself to automate this, giving you a continuously accurate picture of where your engineering effort is really going.
Process vs. Output: A Quick Comparison
Feature | Process-Centric Platforms | Output-Centric (Weave) |
|---|---|---|
Core Focus | DevOps process efficiency (DORA, cycle time) | Engineering output and work quality |
Primary Data | Git/Jira metadata, developer surveys | Code & PR analysis via LLMs & ML |
Key Question Answered | "How fast are we shipping?" | "How much are we outputing?" |
Code Review | Measures review time and rate | Quantifies review depth and quality |
AI Impact | Not directly measured | Quantifies AI's effect on output & quality |
Conclusion
Process metrics like DORA are valuable. They provide important guardrails and help you optimize your delivery pipeline. But they are incomplete. They measure the mechanics of software delivery while ignoring the substance of what's being delivered.
The question isn't just whether you can deploy code quickly. It's whether you're shipping complex, high-value work that moves the needle for your business.
So, are you measuring your team's process, or are you measuring their output?
