I've been doing MFA of my lenses for a few years now, and tried most of the approaches (e.g., I wasted my money on the commercial product Reican FoCal - terrible customer support, never worked on my Windows XP laptop), and found the dot-tune method (either manual, or with ML) to be the best. There is only one feature I wish the ML's implementation of dot-tune had: at the end of the calibration, I'd want to see not just the median MFA value, but also the good MFA interval (the lower and higher values). The reason: MFA values are different at different distances to the target, and at different focal lengths of zoom lenses. And the only way to find the optimal (good for any distance and/or focal length) MFA value is to determine the good MFA min...max intervals at all distance/FL combinations, and then overlap these intervals, and find the more narrow interval (if it exists) which would be present in all of the individual intervals.
A point in case. A couple of days ago I re-calibrated all my 4 lenses. My fast zoom lens (Sigma 17-50mm f2.

was the most tricky as usual. I measured the good MFA intervals by observing the output of the ML on my camera screen. This is not very reliable, but this is the best I could do. I did three distances (1, 3, 8m) and three FLs (17, 30, 50mm). I obtained the following good MFA intervals:
Sigma 17-50 OS: -3 [-3.5,-2]
1m: [-3.5,-2]
50mm: -10, [-18,-2]
28mm: +1, [-7,+9]
17mm: +4, [-3.5,+11.5]
3m: [-8,+0.5]
50mm: -7, [-14.5,+0.5]
28mm: +0, [-9.5,+9.5]
17mm: +0, [-8,+8]
8m: [-7.5,+5.5]
50mm: -2, [-10,+6]
28mm: -3, [-11.5,+5.5]
17mm: +0, [-7.5,+7.5]
As you can see, the biggest tension was at the shortest distance - 1m. If all I had was the "optimal" (median) MFA values reported by ML, then I'd be in a difficult situation trying to pick the best number from -10, +1 and +4. But everything is much more straightforward when you compute the overlapping part of the three intervals, which was pretty narrow: -3.5...-2. All other distances and FLs were consistent with this interval. So I went ahead and chose one value from that interval, -3, and it indeed works very well at all distances and FLs.
If instead I chose to simply average all the 9 median MFA values, I'd get -2. This would still probably be okay, but I feel the interval approach gives much more reliable result.
Can printing the good MFA interval on the camera screen be implemented in ML? I think it'd be a very useful addition, and suspect quite easy to implement.