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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.

OpenTAP has an active and growing ecosystem of partners, developers and end-users.  Ankara-based partner DEICO addresses automotive, aerospace and defense industries with customers in the EMEA region. Having recently joined the OpenTAP partner program.  DEICO team members Hakan Toktaş, Efe Boyacigiller and Batuhan Yilmaz sat down with the OpenTAP team to provide  more information about the company and its investment in OpenTAP.

Keysight has always been at the forefront of the education industry, advancing teaching and learning with innovative solutions. Building on OpenTAP, Keysight enables engineering educators who transform engineering students into industry-ready engineers.

This fourth blog in our series on Total Cost of Owner examines costs for Support and Maintenance, Downtime, Compliance and End of Life.

Supporting open source project code offers greater freedom of choice and reduced lock-in to a single vendor. In many cases, the organization responsible for launching, distributing and/or maintaining the project code also offers commercial-quality distributions and professional support.

In our series about factors that influence Total Cost of Ownership (TCO) for open source test automation, we are investigating how those costs differ from proprietary software, including in-house and commercial platforms. This third blog examines paths from legacy, including proofs-of-concept, training, integration and operationalization.

This second blog in our series examines the factors that influence Total Cost of Ownership (TCO) for test automation. The blog focuses on how those costs differ from proprietary software, whether sourced commercially, created in-house, or built from open source. In this blog, we look at Acquisition Costs, in particular, the eternal Make vs. Buy decision facing IT and engineering management.

OpenTAP makes customization easy by offering a plugin-based architecture. The OpenTAP project includes a range of ready-to-use plugins to support instruments, DUTs, user interfaces, results listeners and more. Plugins also originate from OpenTAP ecosystem participants - hardware manufacturers, software suppliers and end-users. If you have developed an OpenTAP plugin and are eager to share it with other ecosystem participants, read this blog to understand how to host your plugin in the OpenTAP project repository.