Alright, so your team is thinking of adopting Aider, but you want to move past "it feels faster" and get some real, reproducible numbers on how it impacts your actual workflow. Good call. Here's how we set up a simple but effective benchmark.
You'll need:
* A **dedicated Git branch** for the benchmark.
* A **small, representative codebase** – maybe a core service module or a set of common components.
* A **pre-defined task list** that mirrors your team's real work (e.g., "Add error logging to X function," "Refactor Y class to use dependency injection," "Write unit tests for Z module").
Our process:
1. **Baseline:** Time yourself (or a team member) completing each task *without* Aider. Record the time and commit the final, working code for each.
2. **Reset & Run:** Hard reset the branch. Now, tackle the same tasks *with* Aider, using the same prompts/instructions. Record the time again and the final code.
3. **Compare:** Look at total time saved/lost, but also at code quality in the diffs. Did Aider introduce subtle bugs? Did it save more time on boilerplate than on logic?
Key things we track:
* Total elapsed time per task.
* Number of "chat turns" or clarifications needed with Aider.
* Manual intervention required (e.g., having to fix a broken import).
This gives you hard data on if Aider speeds up *your* workflow, not just a synthetic benchmark. It also helps the team learn its quirks. We found it cut boilerplate time by ~40%, but needed careful prompting for complex logic. Your mileage will vary!
What metrics would your team care about most? 🧐
--ash
data over opinions
That's a solid setup for getting concrete data. We did something similar, but we also captured the Git commit hash after each task completion, both with and without Aider. This let us generate a simple diff between the AI-assisted and human-only versions of the same functional change.
We fed those diffs into our standard PR review checklist and found the comparison super useful. It highlighted where Aider was surprisingly good at repetitive patterns but would sometimes miss a subtle conditional edge case a human would catch.
Tracking the number of clarifications needed is key. In our runs, if that number was high, the total time saved often evaporated.
Cloud cost nerd. No, I don't use Reserved Instances.
Capturing the commit hash for a diff-based review is a clever addition. It moves the analysis from "how fast" to "what kind of code," which is where the real TCO discussion happens.
Your point about clarifications directly impacting time saved is crucial. We formalized that as a cost metric. If a task took 10 minutes with Aider but required 5 clarification loops, we logged the total *calendar* time for completion, not just active coding minutes. That often changed the ROI picture entirely.
We found those diffs were most valuable during contract renewal talks with vendors. Being able to show concrete examples of where the AI assistance added versus introduced subtle tech debt gave us much better negotiation leverage.
Buy once, cry once.
The methodology you've outlined is fundamentally sound for isolating the variable of speed. However, a hard reset of the branch for the Aider run might inadvertently skew the results if your tasks have any logical dependencies.
We structured our benchmark with independent, atomic tasks for exactly that reason, ensuring each could be started from the same clean branch state. Otherwise, the order in which you complete tasks with Aider could artificially inflate or deflate its performance if task B builds on changes from task A, which wouldn't mirror the baseline human run.
Another metric we layered on was tracking the LLM's context window consumption per task by logging token counts from the Aider conversation. A high number of clarification turns paired with a high token count often correlated with the AI losing the thread on architectural patterns, which is a useful signal for where its utility drops off sharply.
Data over dogma