Every year for the last four, Keysight has collaborated with faculty at the University of California Santa Cruz (UCSC) Baskin Engineering School to sponsor senior projects in test automation. This year, one of those projects focused on leveraging LLMs (Large Language Models) to improve the timeliness of responses to questions on the OpenTAP Forum.
Wireless network security testing involves evaluating the security measures and protocols of a wireless network to identify vulnerabilities and ensure the network is protected against unauthorized access and attacks. The testing process typically includes several steps and techniques, and so benefit greatly from test automation with OpenTAP
Penetration testing (a.k.a. "pen testing") plays a crucial role in identifying and mitigating security risks. It helps organizations strengthen defenses, protect sensitive data, and improve overall security posture. By leveraging test automation in general and OpenTAP in particular in penetration testing, organizations can improve the efficiency, accuracy, and effectiveness of their security testing efforts, helping them identify and remediate security vulnerabilities before they can be exploited by attackers.
Test automation, like the technology and products it targets, is in a state of constant change.
MarketsAndMarkets reports that the global test automation market size is expected to grow from USD 24.7 billion in 2022 to USD 52.7 billion by 2027, at a CAGR of 16.4%. To accompany the pace of the ever-changing testing landscape, you should be familiar with the latest test automation trends.
This second blog in our series on using OpenTAP for security testing, we take a deep dive into Vulnerability Assessment.
A software vulnerability describes a weakness or flaw in a software system that can be exploited by attackers to compromise the security of the system, its data, or its users. Vulnerabilities can exist in various components of software, including operating systems, drivers, applications, libraries, and firmware.
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.
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.
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.
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.
