Auto-Mock instruments an HTML page and compares a web browser's rendering of the layout to Mockdown's rendering of the layout.

This works in two phases, capturing and evaluation.

Auto-Mock uses a web browser to snapshot several examples of an HTML page's layout. During this phase Auto-Mock captures both training data and testing data.

Auto-Mock evaluates Mockdown on the captured layouts. First, Auto-Mock passes the training data to Mockdown as examples for synthesis. Auto-Mock uses the resulting constraints to evaluate Mockdown's performance on the testing data.

Capturing is complicated because I couldn't get Node to open up a browser window. So as a consequence, we'll use npm run-script browser to compile Auto-Mock into a standalone script and run this script from a browser console.

This script needs to be specialized to each benchmark. In particular edit Bench.ts in the following way:

1. Make sure that the hook at the bottom of the file calls the correct benchmark function, i.e. one of the runXXX functions.
2. Make sure that the benchmark function opens the correct file. It's currently specialized to my local path. The variable to edit is const url = ... in the runXXX functions.

Once this is done, run npm run-script browser, which copies Bench.ts into a standalone script in your clipboard. Then, open up a web browser console and copy-paste the output into the console. After a number of pop-ups the benchmark results will be present in the console.

Copy-paste this result into a new JSON file in the bench-cache/ subdirectory and give a name as you like.

Evaluation is straightforward so long as mockdown is currently running and the website has been captured into a file in bench-cache/. The command for this is npm run-script mock -- --fp='<captured_file.json>' --filter='<base, none, hier>' --range <low_width> <high_width>. For example, like so: npm run-script mock -- --fp='yoga-empty.json' --filter='base' --range 400 900. The output is a list of normalized RMS values where each x entry is that particular new width of the training set, and the y is the average normalized RMS for the trees in the training set.