Blog

Every year brings changes in the open source ecosystem. Myriad new projects, new applications and frameworks, new foundation working groups and new business trends. 2023 was no exception.

Open source software is no longer viewed as a novelty; indeed it is today thoroughly mainstream, to the point of banality. Nonetheless, the emergence of new projects and the progress of existing ones, the use of open source licenses, the role of open source code and the ups and downs of the open source ecosystem continue to present both challenges and opportunities.

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.

Cybersecurity sits at top of mind for IT professionals and these days, even for device manufacturers.  Cybersecurity testing includes a range of assessments and evaluations that focus on various aspects of security posture. These tests help identify vulnerabilities, weaknesses, and potential threats in systems, networks, and applications.

This first blog in a series calls out the various types of cybersecurity testing and how and if OpenTAP can facilitate and control each.

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.

Test automation and the Internet of Things (IoT) are distinct but interconnected domains The relationship between the two primarily lies in how test automation can be used to ensure the quality and reliability of IoT devices and systems.

In complementary fashion, modern intelligent, connected test instruments share attributes with other IoT edge devices – sensor-centric mono-functionality, remote access and control, cloud-based analytics, etc. – and so themselves participate in the Internet of Things.

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.

Artificial Intelligence (AI) and Machine Learning (ML) are bringing new functionality to applications across information technology. Projects like ChatGPT that employ large language models, and various image creation engines garner popular attention, but AI and ML have potential to enhance test and test automation in myriad ways.

This blog explores complementary aspects of enhancing test and test automation with artificial intelligence and machine learning, in particular, how AI and ML can enhance test and test automation.

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.

This blog demonstrates how hosting the OpenTAP test automation engine itself on ARM-based systems is a relatively straightforward task. Since OpenTAP is built with .NET, it enjoys the hardware abstraction provided by the Microsoft application framework, with very few hardware-specific dependencies or idiosyncrasies. As examples, the blog shows how to target an Apple M1 host running Ubuntu Linux, an ARM64-based Raspberry Pi system, and an M1 Pro-based MacBook Pro running MacOS.

Testing and test automation usually conjure visions of software and hardware under test on a test bench in a lab or on a production line. The involvement of human actors can seem quite secondary and distant from executing test plans and running through test steps.

In practice, test engineering is a very human endeavor: humans design the systems under test, specify testing criteria, implement test code and evaluate test results. And they don't perform these tasks in isolation.

Every year for the last three Keysight has collaborated with faculty at the University of California Santa Cruz (UCSC) Baskin Engineering School to sponsor one or more senior projects in test automation. This year, that project focused on utilizing OpenTAP and OpenTAP plugins to control a collaborative industrial robot arm (cobot). The UCSC team faced a range of educational, logistical and technical challenges and met each with intelligence and aplomb.

Cost calculation for legacy in-house test automation platforms is seldom a simple task; costs for in-house test automation are embedded in s/w and h/w development budgets. if itemized at all, testing costs fall into product and QA/QC budgets But the cost of developing and maintaining a test platform is almost never itemized because it’s not part of the product specification.