CodeAssurance

Installation Instructions:

Step 1: Download Paclets:

CodeAssurance-1.0.0.paclet

OnsocLicenseManager-1.0.0.paclet

(Avoid downloading to the default download folder due to occasional permission issues)

Step 2: Install Paclets

PacletInstall["path/to/CodeAssurance-1.0.0.paclet"]

PacletInstall["path/to/OnsocLicenseManager-1.0.0.paclet"]

Step 3: Load CodeAssurance

<<CodeAssurance`

The first time the above command is executed a dialog appears (see l.h.s. image).

Enter the license key payhip generates with this order.

Once licensed all subsequent calls of <<CodeAssurance` load the package in the normal way.

Documentation

Once installed CodeAssurance's documentation appears in Mathematica's Documentation Center. A selection of snapshots of these appear in the l.h.s. sliderview or alternatively, full web-versions are available including a function guide and tutorials for performing large-scale testing or setting up seamless paclet-workflows.

License Duration & Upgrades

The license is valid for one year including access to any upgrades.

PacletObject["CodeAssurance"]["Version"]

gives the current version installed on your system. If a higher version appears on this page, an appropriate local paclet configuration can ensure CodeAssurance is upgraded automatically.

Uninstalling

PacletUninstall["CodeAssurance`"]

PacletUninstall["OnsocLicenseManager`"]

uninstalls both packages from your system.

Philosophy

CodeAssurance provides extra tools for assuring the robustness of large computational systems in the Wolfram Language. An important aspect of this assurance includes organizing, running and publishing of a packet's unit-tests. New functions like TestSummary, TestFile and TestFiles encourage the development of comprehensive testing suites.

Further, functions like $DeveloperDirectory, DeveloperOn, DeveloperOff promote a developer-mindset for establishing paclet-workflows based around cycles of calls to CreateDeveloperPaclet, PacletBuildInstall, PacletDeploy.

The future of computation all seems to lie in combining deterministic software practices with the stochastic imagination of generative AI.