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@@ -44,11 +44,22 @@ The benchmark results (configurations and corresponding non-functional propertie Once the benchmark is done, the observations can be compressed into a single file by running `.../dfatool/bin/analyze-kconfig.py --export-observations .../my-observations.json.xz --export-observations-only`. Depending on the value of the **DFATOOL_KCONF_WITH_CHOICE_NODES** environment variable (see below), `choice` nodes are either treated as enum variables or groups of boolean variables. +Most approaches in the literature use boolean variables. ### Generating Models -To be documented. -In the meantime, we have a short [video example](https://ess.cs.uos.de/static/videos/splc22-kconfig-webconf.mp4). +[analyze-kconfig.py](bin/analyze-kconfig.py) builds, evaluates, and exports NFP models from explore-kconfig measurements. +Command-line options and environment variables determine which kind of NFP model it generates. + +For example, the following command generates a CART model for busybox. +Classification and Regression Trees (CART) are capable of generating accurate models from a relatively small amount of samples, but only annotate important features. +Hence, after loading a CART model into kconfig-webconf, only a small subset of busybox features will be annotated with NFP deltas. + +``` +DFATOOL_DTREE_SKLEARN_CART=1 DFATOOL_PARAM_CATEGORIAL_TO_SCALAR=1 DFATOOL_KCONF_WITH_CHOICE_NODES=0 ~/var/ess/aemr/dfatool/bin/analyze-kconfig.py --export-webconf busybox.json --force-tree ../busybox-1.35.0/Kconfig . +``` + +We also have a short [video example](https://ess.cs.uos.de/static/videos/splc22-kconfig-webconf.mp4) illustrating this workflow. ## Dependencies |