Blog Category

Artificial Intelligence (AI) is a focal domain for developers, for end-users and for the venture capital community.  It’s as hot a commodity as Linux and open source were two decades ago.  But AI and open source share more than just hype.  Across natural language processing (NLP), Machine Learning (ML), Computer Vision, and Robotics, both AI and open source drive the democratization of technology, and open source is helping to drive the utility and ubiquity of AI platforms and applications.

In October, the GNU project and the larger category of Free Software turned 40 years old. This blog explores the impetus and nature of Free Software, the philosophy, projects and licenses involved, how Free Software compares to Open Source Software, and the impact of Free Software and Test Automation.

Free Software was once considered radical, even anathema to organizations wishing to preserver rights around intellectual property. When introduced in the 1980s, it was not expected to be particularly significant or even to survive.

Well, survive it has, and has flourished.

Often, organizations large and small get the urge to craft their own FOSS licenses. This desire arises and persists despite the existence of over one hundred OSI-approved licenses, plus over two thousand other self-styled FOSS licenses. These FOSS-ish licenses do not earn a place on the OSI list because they are either too similar to existing approved licenses or they violate principles of the Open Source Definition and the Free Software Definition.

This blog addresses motives for writing new licenses and why you should resist the urge.

Open source is everywhere. It is highly visible, easy to acquire, use and deploy. The facility of acquisition and use can give open source, collectively, the appearance of a tech candy store, tempting developers and end-users to take fists full of open source code, sometimes without regard for the implications for intellectual property, security and general overhead.

Open source (including Free Software may indeed be free to acquire, use and deploy, but that usage is accompanied by a set of risks, some of which are shared with traditional proprietary software, while some are not.

Open Source software increasingly dominates application stacks across industries. Enterprise software builds on Linux, Jenkins, Kubernetes and other platforms and middleware; device software looks to Android, Linux, GNU, and myriad other platforms and tools, as well as open source code used to test those devices.

Despite the popularity and ubiquity of open source, many organizations still retain misgivings about using it. This blog examines those misgivings, debunking some concerns, and examining the truth behind others. Whenever possible, the topics are related to open source in Test Automation

This blog examines how companies and individuals make money and/or enjoy economies by using open source software (OSS). The blog highlights multiple business and technical strategies for monetizing open source in general, and examines the commercial role of OpenTAP and opportunity for the platform in the test automation ecosystem.

Open source software (OSS) simultaneously enjoys a superb reputation as a source for cybersecurity tooling and less-than-stellar (if undeserved) repute for suffering from vulnerabilities that facilitate data breaches and other attacks. Given that OpenTAP is an active open source project, members of the OpenTAP ecosystem often inquire about the security of this test automation platform and of OSS in general.