“Faster, better, cheaper: pick two” is an old adage. However, by integrating automated intelligence across the development workflow, you may be able to achieve all three.
Today’s newest electronic products are increasingly complex due to evolving standards, applications distributed across multiple cloud environments, and the acceleration of an “all in one” expectation from users. This increased complexity directly impacts the electronic design, development, emulation and test coverage for new products, which puts increased pressure on development teams.
With so many potential vectors to test, there is an expectation that automating design and test procedures with intelligent insights across the workflow–in essence, automating intelligence–is mainstream. However, according to a recent Forrester study commissioned by Keysight Technologies, 89% of companies still employ manual processes, with only 11% of companies fully automating their test matrices. While full automation adoption is low, companies do see its value, with 75% reporting some automation and nearly half wanting to fully automate in the next three years.
AI, machine Learning and digital twins increasingly desired for complex electronic system development
In December 2021, Keysight commissioned Forrester Consulting to evaluate the use of data integration, analytics, artificial intelligence (AI) and machine learning (ML) in a typical product development cycle. Forrester surveyed over 400 development leaders and asked them a series of questions related to how much they currently employ AI and ML in their product development process.
On the surface, most organizations reported they are satisfied with their current development approaches, with 86% being moderately to very satisfied. However, those same organizations reported that 84% of projects and designs are either complex, multilayer sub-systems or integrated systems, which are not being tested.
Despite what initially looks like satisfaction, the study found that companies feel the pressure to add more automation and intelligence to the electronic design process, especially when asked about the future.
Only 1 in 10 companies currently use full design and test automation in their development process, but the COVID pandemic has accelerated adoption of remote development and automated test sequencing. There will, most likely, also be a higher use of digital twins as development teams strive to continue working together from different locations.
Digital twins and emulation: Electronic design’s new paradigm
Hardware developers have long relied on emulation environments as part of the design process before prototyping. Using software-driven emulators, or digital twins, reduces the number of design variables by allowing them to measure the impact of different operating environments, conditions, and protocol evolutions against “known good” references.
Similarly, software developers use agile scrum methods and test in virtual emulation sandboxes to build and deploy new features incrementally, while limiting the number of variables.
The rising complexity of electronic product interactions–communication protocols that evolve, cloud platforms that evolve, continuous software and firmware updates–pose real challenges for developers as each introduces a slew of new variables for testing. Using test automation and continuously updated digital twins wherever possible enables development teams to test more variables and reduce the risk of non-performance for a specific design.
Automating intelligence across the electronic design workflow
Test automation is rapidly becoming a must-have requirement. Currently, a fully manual test plan based on human data entry, some python or graphical programming, and Excel spreadsheets can only cover a small portion of possible user stories. Each must be updated manually for every software release, lengthening the electronic design cycle.
However, while test automation software is part of the answer and definitely needed, it is not enough. Test automation is only as good as the analytics and insights they produce. In the Forrester survey, respondents disclosed that their test routines covered “more than needed” just over half the time. While test automation can help reduce the time it takes to test, it doesn’t solve the test reach, quality, and coverage question. Automating intelligence across the design workflow, with farther-reaching test sequences, all backed with analytics and insights, solves both test speed and test reach at the same time.
Automating intelligence is a software model that builds on the industry’s deepest measurement technologies and simulations to provide rapid insights that developers can use to get to market with greater speed and lower risk. Whether measuring power and ground, waveform signal quality, high-speed data I/O’s, network integrity, or application delivery, we must consider what it will take to for customers to speed the development processes.
What does automating intelligence success look like?
Faster, better, cheaper – pick two is an old adage when people would strategize new developments. Assuming nothing else changes, that might still be true. However, by integrating automated intelligence across the development workflow you may be able to achieve all three.
- Faster: Ability to reduce product time to market
- Better: A higher-quality product that increases customer satisfaction
- Cheaper: More agile and efficient product development cycles
Development teams that have already adopted this approach are reaping the benefits today. Whether a product development involves emerging electronics using the latest wireless communications standards, high-speed data transfers, complex cloud networking, or dealing with distributed software application delivery, the focus is the same. Structure your lab design and test solutions to deliver insightful analytics at each stage.
Power them with AI and ML so they are always exploring ‘what else’ needs work. Automate your development environment just like your do in manufacturing to minimize development timelines while ensuring the best possible product performance.
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