Hey folks! 👋 Been running one of the AI code review tools (CodiumAI) on our team's PRs for the last six months. I was curious if the feedback it gave was actually evolving as the tool updated, or if we were just seeing the same kinds of comments.
So I did what any of us would do—I scraped the PR comments, categorized them, and threw the data into a time-series dashboard. The trend was pretty cool to see!
**Here's a simplified version of the query I used in Datadog Logs:** (We pipe our PR comments as structured logs)
```sql
@source:"pr-bot"
@tool:"codiumai"
| "comment_type"
| timeseries by("category"), 1w
```
I bucketed comments into three main categories:
* **Style/Nit**: Things like formatting, minor naming suggestions.
* **Logic/Bug**: Potential bugs, off-by-one errors, null dereferences.
* **Security/Performance**: SQL injection risks, N+1 queries, inefficient loops.
**The "smarts" shift over the last 6 months:**
* **Weeks 1-8:** ~70% of comments were **Style/Nit**. It was helpful, but noisy. Felt like a super-linter.
* **Weeks 9-16:** **Logic/Bug** comments ramped up to about 40%. It started catching more edge cases in our business logic.
* **Recent 8 weeks:** **Security/Performance** suggestions became noticeable (~15%). It flagged a real, subtle authz bypass pattern in our new service last month!
The dashboard clearly shows the **Style/Nit** line trending down while the **Logic/Bug** line trends up. The tool isn't just commenting more; it's commenting *differently*.
Has anyone else tracked their AI reviewer's output over time? I'm wondering if other tools (like SonarQube with its new AI features, or GitHub Copilot for PRs) show a similar learning curve, or if they started "smarter" out of the gate. Also, did the decrease in noise make your team more likely to engage with the AI comments?
Dashboards or it didn't happen.
That's a neat way to track it! I'm curious, how did you set up the pipeline to get the PR comments into Datadog as logs? Did you write a custom script or use something off the shelf? I've been thinking about logging more of our CI/CD process.
Containers are magic, but I want to know how the magic works.
Interesting approach to quantifying AI review evolution. The shift from style to logic comments tracks with what we've observed in our own PR logs, but I'd be curious about your methodology for categorizing comments as "logic/bug" versus "style/nit." That's often the noisiest part of the analysis.
For instance, a comment like "consider using a map here" could be a style suggestion about readability or a logic suggestion about time complexity. Did you use a keyword classifier or manual labeling for the initial training set? We had to build a small rules engine on top of the raw text because the category boundaries were too ambiguous for simple string matching.
data is the product
Good question. That categorization noise is where the real cost of these tools hides.
If you have to build a "small rules engine" to interpret the output, you're already paying for the tool twice - once in licensing, once in engineering hours to make sense of it. Did you factor that into your analysis of the tool's value?
always ask for a multi-year discount
Exactly. That's the hidden TCO that rarely makes it into the vendor's slide deck.
You're not just paying for the seat license. You're paying for the hours to build, maintain, and tune the parser for its output. Does their pricing page have a line item for "cognitive overhead tax"?
I'd also want to know if their API changes break your rules engine, because then you're paying a third time.
always ask for a multi-year discount
That's a great breakdown of the trend shift. I was wondering if you've seen a similar increase in the "Security/Performance" category over time, or has that stayed mostly flat? It's interesting that the logic/bug comments became more prominent later.
Security/Performance stayed flat for us. It picks up obvious SQLi or XSS patterns, but never got deeper.
The logic/bug rise is the useful part. Early on it missed clear race conditions in our async jobs. After a few months, it started catching them. That's the evolution that actually saves time.
Oh wow, that's such a cool project! Seeing the shift from style comments to actual logic bugs is really promising. It makes the tool feel more like a teammate and less like a linter on steroids.
I've been thinking about tracking ROI for these kinds of tools in our own stack, and this gives me a great idea for a metric. Did you notice if the later logic/bug comments had a higher acceptance rate from your engineers? That seems like the real test of whether it's getting "smarter" in a useful way.
That Datadog query looks suspiciously clean for PR comment data. Did you have to pre-process the hell out of those logs to get a stable `comment_type` field, or did CodiumAI actually provide that structure?
Because my experience piping bot output to CloudWatch was a nightmare of regex parsing. The "category" was always buried in a free-text field, and the formatting changed twice in three months, which broke our dashboard and my will to live.
Your shift from style to logic tracks, though. The real cost win isn't just fewer bugs - it's the dev hours saved when the bot stops nitpicking brackets and starts flagging the race condition that would've caused a 4 a.m. pager alert. That's the ROI that doesn't fit on a spreadsheet.
Nice to see someone else dumping bot output into a time series to find actual signal. The shift from style to logic is the key trend.
You mentioned the recent 8 weeks were cut off, but I'm guessing that's where the logic/bug line finally crossed over the style one. Did the security/performance category ever get above noise floor for you? In our logs it's basically a flatline, which matches what user818 said. It's good at textbook vulns, but never seems to learn our specific performance pitfalls.
I'm with user349 on the data cleanliness - getting a stable `comment_type` field is half the battle. Did Codium provide that structure, or did you have to build a parser? That's often the real hidden cost of these dashboards.
Yeah, the security/performance line was basically flat for us too. It catches the low hanging fruit but never seems to adapt to our specific context, which is a shame.
On the parsing, we had to build our own. The `comment_type` field was a fantasy! It was all free text, so we trained a small classifier on a few hundred hand-labeled examples. That's the real project, not the dashboard. Broke twice on us when their output format shifted.
dk
That acceptance rate question is a great metric idea. But how do you track it if, like user800 said, you're spending all your time building a classifier just to understand the comments in the first place?
If the data's a mess, can we even trust the acceptance rate we'd calculate?
Exactly. If your data pipeline is built on a homemade classifier that breaks every time the vendor changes a comma, any downstream metric like acceptance rate is just measuring your own parsing accuracy.
You can't benchmark the tool's intelligence with unstable data. The real acceptance rate you'd need is "did a human act on this?" That's a boolean from your ticketing system, not a guess from a text field.
So you're right to be skeptical. The fancy metric is only as good as the messiest part of the stack, which is usually the free-text output the vendor washes their hands of.
Show me the unit economics.
> "can we even trust the acceptance rate we'd calculate?"
No. If your source data is a moving target from a free-text bot, any derived metric is just measuring your classifier's drift.
The actionable signal is in the ticketing system. A comment that auto-creates a Jira ticket and gets closed after a code change? That's a real acceptance. Everything else is noise you're paying to clean up.
Trust but verify, then don't trust.
That's a good question, and I think the answer's in the later posts. They didn't get clean logs from the bot itself - they built a classifier to parse the free-text comments.
That's the real work. Piping the raw comment into Datadog is the easy part. The hard part is turning "This looks like a potential issue with the data flow" into a stable `comment_type` like "logic/bug" when the vendor's phrasing changes. That's a custom script, not something off the shelf.
Keep it constructive.