The rise of artificial intelligence (AI) and machine learning (ML) is changing the electronics exam landscape. Electronics companies around the world are under pressure to launch ever more sophisticated products at breakneck speed while adhering to strict quality standards. Today’s system-on-chip (SoC) designs include a complex mix of billions of transistors and complex firmware, making comprehensive testing a challenge. Traditional approaches that rely on pre-configured test vectors and deterministic algorithms are often too far ahead in scale and complexity. AI and ML are currently in the spotlight and are expected to revolutionize autonomous testing. By applying probabilistic reasoning, pattern recognition, and adaptive algorithms, these technologies save weeks of development cycles and improve defect detection rates by as much as 25 to 40 percent, according to industry insiders.
AI efficiency versus manual testing
The story of testing in the electronics field has a strange beginning. In 1945, engineers traced the malfunction to a real moth buzzing inside a computer’s relays. Ten years later, testing has become a specialty. However, even as methods became more sophisticated, manual testing remained labor intensive, subject to human oversight, and often failed to keep up with the demands of modern design. Let us usher in a new era where technology, not tradition, takes the lead, and the pursuit of precision blurs the line between humans and machines.
AI/ML challenges for testing
While the integration of AI and ML will undoubtedly speed up testing procedures, the real question will be determining how much data is needed to feed the AI for thorough and accurate testing, primarily for startups and small teams.
“One of the main challenges in developing AI-driven test systems is ensuring high-quality, unbiased data, as AI models rely heavily on reliable inputs. Maintaining transparency in AI-driven decision-making and balancing automation with human expertise are also important, especially in highly regulated and safety-sensitive industries. NI addresses these challenges through an open, software-defined test architecture and engineer-driven AI integration. By standardizing data acquisition and validation at the source, NI ensures data consistency, traceability, and transparency before applying AI. This foundation also makes it modular. Easily connect hardware and open software to a variety of sensors, devices, and communication protocols, enabling efficient integration with new IoT technologies. Edge computing capabilities further support real-time data processing closer to IoT endpoints, reducing latency and improving responsiveness as IoT systems continue to evolve. ”
The “black box” problem is considered one of the most central challenges when incorporating AI into autonomous testing. This refers to the difficulty in understanding how complex models, especially deep learning systems, arrive at decisions, treating them like opaque boxes with only inputs and outputs visible. Its complex, multi-layered processes are difficult to interpret and can hide bias and errors, leading to issues of trust, fairness, accountability, and debugging.
“At each step, you must decide which parameters to optimize. The goal is to do as many tests as possible with as few test patterns as possible to control cost,” explained Fadi Maamari, vice president of engineering for hardware analysis and test at Synopsys.
Benefits of AI in testing solutions
“AI is already having a significant impact on adaptive test optimization and anomaly detection,” said Eduardo Estima de Castro, senior manager of research and development engineering at Synopsys. “Machine learning helps prioritize high-value test patterns, reduce test time, and identify overall yield issues. It also allows test limits to be adjusted in real time, improving output quality. These capabilities provide significant efficiency gains in high-volume manufacturing.”
AI has the ability to generate a wide range of real-world scenarios for testing. The wide range reduces the chance of error and increases the range of creativity for engineers.
NLP-driven technology analyzes requirements and user stories and generates test cases that meet defined criteria. This approach ensures complete test coverage while reducing the manual effort required to write test cases. Additionally, AI and machine learning algorithms may prioritize test cases based on risk, severity, and defect history. This prioritization ensures that the most important tests are run first, optimizing testing efforts and increasing coverage. AI-powered technology can also perform root cause analysis to identify the root cause of failures and make meaningful recommendations for remediation. AI and ML technologies automate visual testing by comparing aspects of the application (e.g. UI components) with predicted results. The most important feature is the ability to continuously learn from new data and adapt to changing requirements. This continuous learning improves the accuracy and effectiveness of test automation technology, resulting in continuous improvement of software testing.
Related core technology
Different AI models have different designs, but they highlight the core features of this concept. “The foundation of this test software is a software-defined, modular test architecture combined with AI-driven data intelligence. This design enables an autonomous test system that adapts to changing requirements, learns from data, and improves performance over time,” said Shitendra Bhattacharya, Country Head and Director, Emerson NI India. “Open test software platforms such as LabVIEW, TestStand, SystemLink, and FlexLogger allow engineers to design, customize, and reuse test logic across products and industries while integrating.” This is complemented by modular, software-connected hardware such as PXI, CompactDAQ, CompactRIO, and USRP, which allows systems to be upgraded or reused using software alone, and an AI-enabled data architecture that embeds analytics directly into test workflows, enabling intelligent analytics to detect patterns, predict and flag failures. Abnormalities occur quickly. ”
Adoption rate and use cases
AI is widely used to test software embedded in electrical equipment and falls under the broader scope of AI in software testing. The main areas of implementation are:
self-healing test: AI systems detect changes to your application’s UI or code and automatically update test scripts, eliminating the need for maintenance.
Test cases and data generation: AI can create extensive and realistic test data and scenarios based on user stories and historical data, including edge situations that human testers might miss.
Predictive analytics: Artificial intelligence analyzes historical data to predict where problems are most likely to occur, allowing QA teams to focus testing on high-risk areas.
“The adoption of AI-enabled autonomous testing is already accelerating, especially in industries where electronic complexity is rapidly increasing, such as semiconductors, automotive and EVs, aerospace and defense, and advanced manufacturing.Currently, AI-enabled autonomous test is being used in R&D and validation environments where AI helps analyze large test datasets, and in high-volume production testing where automation increases speed, consistency, and yield. Going forward, we expect AI-driven testing to become the norm as systems become too complex. “This shift will reduce time to market, improve product reliability, and free engineers to focus on innovation and system-level problem solving rather than repetitive testing efforts.” Shitendra Bhattacharya, Country Head and Director, NI India explains.
Manufacturers use AI-powered computer vision systems to inspect electronic components such as PCBs for defects such as misalignment, microcracks, and defects in solder joints. These systems can accurately and quickly identify anomalies that are difficult to spot with human inspectors or traditional camera systems, leading to zero-defect manufacturing goals.
Agilent used AI vision tools to reduce defect rates by 49% in one application over four months. Companies such as Jidoka and Averroes offer specific AI tools for PCB inspection that integrate with existing manufacturing lines and learn new defect types from minimal sample images.
What will the future hold?
The future of AI and ML in test automation is bright, with continued advances poised to further revolutionize testing in the electronics industry. Here are some emerging trends to keep an eye on.
- autonomous testing: The ultimate goal of AI and ML in test automation is to achieve autonomous testing, where the entire testing process, from test case generation to execution and analysis, is fully automated with minimal human intervention. This level of autonomy is still a work in progress, but ongoing advances are bringing it closer to reality.
- Strengthen collaboration: AI and ML should foster collaboration between development, QA, and operations teams by providing actionable insights and predictive analytics. These insights enable teams to make informed decisions, refine testing strategies, and improve overall software quality.
- Integration with emerging technologies: AI and ML will increasingly be integrated with other emerging technologies such as the Internet of Things (IoT), blockchain, and edge computing. This integration enables comprehensive testing of complex, interconnected systems, ensuring reliability and performance in real-world scenarios.
• Continuous learning and improvement: AI and ML models continue to evolve, learning from new data and adapting to changing requirements. This continuous learning improves the accuracy and effectiveness of test automation tools and drives continuous improvement in software testing.
Author: Shreya Bansal, Associate Editor
