On Tue, Nov 3, 2026 at 11:37 AM Mark Thomas via dev
<[email protected]> wrote:
>
> On 02/11/2026 13:57, Rémy Maucherat via dev wrote:
> > On Mon, Nov 2, 2026 at 2:27 PM Dimitris Soumis <[email protected]> wrote:
> >>
> >> On Mon, Nov 2, 2026 at 2:24 PM Rémy Maucherat via dev 
> >> <[email protected]> wrote:
> >>>
> >>> On Mon, Nov 2, 2026 at 12:41 PM Mark Thomas via dev
> >>> <[email protected]> wrote:
> >>>>
> >>>> All,
> >>>>
> >>>> The start of the month is here so we should be thinking about tagging
> >>>> the November releases.
> >>>>
> >>>> The various AI reviews have resulted in significantly higher numbers of
> >>>> commits and PRs. There are also a handful of open bugs.
> >>>>
> >>>> Normally, I'd aim to review all of the commits, resolve the new PRs and
> >>>> the open bugs before release.
> >>>>
> >>>> Currently, I am 2-3 weeks behind and I am unsure what to do.
> >>>>
> >>>> Any BZ issue or PR that relates to an enhancement can wait until the
> >>>> next release but that still needs those PRs and bugs to be triaged. The
> >>>> commit reviews are harder. I was hoping that I'd be able to work through
> >>>> them fairly quickly but that isn't the case. I am finding additional
> >>>> fixes that are required or regressions quite frequently. Many of these
> >>>> could be classes as no worse than the original code but there are
> >>>> occasionally more serious regressions.
> >>>
> >>> Yes, the extra fixes are very useful. It would be better if they were
> >>> not needed of course (working on that ...).
>
> My reviews have speed up considerably this morning. There is a clear
> correlation between code complexity and speed of review / further fixes
> being required. That suggests to me that we need to be using more
> powerful models for the more complex code. Maybe look at using the free
> AI credits for those models that have been offered to ASF committers?

Obviously if the issue is simple, it will work out better ! (also
applies to me if I try to fix an issue !)
To be honest, I think the models I have on hand are more than good
enough and are able to do complex tasks.
However, there are some issues and I need to use them better in some
cases. The main limitation I have to work around when I'm using a
"small" "fast" model is that the context is limited to 256K, and for
some models the quality degrades a lot after compaction. Also my
review step was very suboptimal. And of course my manual review step
after the changes is far from perfect (= it's the worst).
Although the HTTP/3 experiment was written with the small models, the
review steps were done by "big" models (with 1M context; technically
only 512K was ever reached). The main reason is that the components
are large and would lead to non stop compaction and useless results on
small models.

I expect the small open models will show another capability leap
simply by increasing the context. I know I can use some stuff like
rope with the current models, but it seems accuracy goes down.

> >> To me, it seems clear that the whole development process has changed. 
> >> Therefore, I suggest changing the review process to introduce a risk-based 
> >> approach. For committed reviews, maybe focus review on potential 
> >> high-impact areas and limit nonessential changes ~1 week before tagging.
>
> I agree the development process is changing. We can't not review code
> and I've seen enough obscure bugs in what I thought was non-critical
> code lead to a vulnerability that I'd be very cautious of reducing the
> level of review to any code.
>
> My current thinking is using more powerful models will enable the
> initial fix to be cleaner. That addresses the time problem by reducing
> the time needed for the subsequent review.

Ok.

> >> If we wanted to take it one step further, it might be worth considering 
> >> altering the release cycle from monthly to something more frequent so 
> >> reported regressions can be fixed much faster. To reduce the overhead of 
> >> this, we could also explore utilizing the Apache Trusted Releases project.
>
> Increasing the release rate to address an increased volume of
> regressions seems like the wrong approach to me. I'd rather focus on
> reducing the regressions (see above).
>
> I'm also not sure our user base could keep up. I'm fairly sure only a
> very small percentage of our users are keeping up with the latest
> releases as it is. I agree that the way things are going they do need to
> do a better job of keeping up.
>
> >> AI is accelerating development, and I think our processes should evolve to 
> >> accommodate that.
>
> It is still relatively early days with AI. At the moment, I think we are
> applying AI inconsistently across the development process so it is
> speeding up some areas and creating bottlenecks in others. I think we
> need to apply a little more AI upfront - particularly to the changes to
> more complex areas - to reduce the time spent on reviews which is where
> the bottleneck is currently.
>
> > LLMs seem to be making things different overall (for the worse, of
> > course, given the volume of very very hard to find CVEs that would
> > have never been found without it).
> > Code reviews produce (lots of) defects (PRs are now all produced by
> > LLMs and are basically the same thing). These defects are really non
> > critical: we know Tomcat is stable and worked fine 6 months ago. So
> > some edge cases nobody ever cared or ran into are getting fixed,
> > because once we know about them, we have to fix them. However,
> > individually, the value for each seems infinitesimal. Only if
> > everything gets fixed, we could assume a marginal reliability
> > improvement (not to be underestimated either since the last bit of
> > reliability requires a maximum amount of effort to achieve).
>
> Agreed. I do wonder if some the claimed edge cases are even possible at
> all but in most cases it takes less time to fix the edge case that it
> does to reason it doesn't apply.
>
> > We also have new features that are possible. Nobody would have ever
> > cared about doing a Manager 2.0 webapp, well, LLMs do a decent job at
> > that. This seems useful to users (webapp is "better" than the existing
> > one), but the benefit is also slim.
> > We can also do features that nobody really wants (QUIC). Similar low 
> > benefit.
>
> I would argue that features like TLS support for clustering do provide
> real benefit and there is no way I could have implemented that in the
> time I did without AI support.

Oh yes, I forgot about PSK. My bad :)

> I did briefly start to look at using AI to do refactoring of some of the
> more complex code to simplify it but ran out of time before I made any
> real progress. I hope that this is an area that we will be able to come
> back to.

This is clearly an area where you need a consistent stable model, not
necessarily the best, but with a very large context. Trying to
investigate on (or review) a large chunk of code requires it.

Rémy

> > Overall: not great, but I feel like I have to do it.
>
> Ack. I feel the same way.
>
> > Rémy
> >
> >>>>
> >>>> Do we tag anyway and address regressions as they are reported?
> >>
> >>
> >> +1
>
> I'm not a huge fan of this approach but I recognise we might need to do
> this.
>
> >>>> Is there a better way to triage a large number of commits for closer 
> >>>> review?
> >>>>
> >>>> Does delaying the tag gain us anything?
> >>>
> >>> The [VOTE] was posted on the 6th last month, so there's time.
>
> With the speed I am currently working through the reviews, I am a lot
> more hopeful. I am ignoring the jdbc-pool changes for now on the grounds
> that DBCP is the default and users that find a regression in jdbc-pool
> can always use DBCP (or a jdbc-pool JAR from a previous Tomcat version).
>
> >>> There are 5 BZs to look at right now, so it's not that bad. To be
> >>> honest, even AI centric projects have given up long ago.
> >>>
> >>> Note: the BZ "Long Format" button allows saving the html and
> >>> triaging/reproducing issues fast (usually at least). I used it to
> >>> triage the jdbc-pool BZs.
>
> Good to know.
>
> Mark
>
>
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