IBM has a great smartpaper online called Driving ahead with ASPICE that discusses what automotive engineers are up against by way of current technology in the vehicles they design.
Namely, throughout the course of the design process, it’s become routine for an automaker’s engineering groups to have to navigate about a hundred million lines of code. Or more. AI and machine learning are now solidly in the mix, too. As IBM’s smartpaper frames it, the advanced capabilities of AI and machine learning, along with IoT, are what enable self-driving cars to make decisions, computerized systems to detect potential failure, real-time geospatial information to stream into the cockpit, and infotainment systems in your car to rival any system in your home.
And that’s on top of engineering groups increasingly having to accelerate time to market and comply with industry standards such as ASPICE, or the Automotive Software Performance Improvement and Capability Determination.
Standards like ASPICE, as well as others like Capability Maturity Model Integration (CMMI), are necessary to measure design process quality and gauge vehicle safety and security. For any engineering and development team in the automotive industry, adhering to these standards therefore is crucial. Doing so can result in a more efficient design process and higher-quality end-product, usually marked by decreased failure and repair rates.
But as IBM also notes, adhering to regulations like ASPICE isn’t just advised, it’s mandatory. The challenge to automotive engineers, then, is this: “How do you comply with exponentially increasing sets of regulations, created by numerous governing bodies, with requirements that could make your products prohibitively expensive?”
AI and machine learning to improve engineering and development
For today’s vehicles themselves, AI and machine learning have already made their way into autonomous cars, infotainment systems, failure detection systems and the like. Yet everything that fuels this intelligence comes back to the hundred million lines of code that engineers must deal with to design intellect into a vehicle in the first place. This is where, IBM says, AI and machine learning can make another impact. Their smartpaper cites three aspects of the engineering and development process in particular.
Requirements: With AI and machine learning providing a foundation for requirements authoring, engineering teams can implement Natural Language Processing (NLP) to help avoid inaccuracy and ambiguity. NLP can capture data from project records, design notes, supplier parts records and other sources by the thousands and keep data in the needed requirements context, including where requirements intersect with ASPICE and other standards. NLP can also be helpful in combing data to predict root causes of vehicle system failures and prescribe preventive actions, a process already being used by several OEMs in the automotive industry.
Collaboration: Global teams of teams create global systems of systems, leveraging AI and machine learning for consistency across a project. (Well put, IBM!) In the same way as authoring requirements, AI and machine learning can stem inaccuracies and ambiguity in how global teams interpret data and incorporate its findings into the design process. Think of managing a hundred million lines of code and compliance to industry standards. AI and machine learning can keep teams on the same page, especially when they’re operating in different cultures and all corners of the world.
Testing: All those requirements, systems, lines of code, engineering teams and standards, including ASPICE. Here again, AI and machine learning can help align various moving parts and stakeholders to expedite decision making during testing based on quality requirements.
It’s ironic that, while IoT, AI and machine learning are forcing automotive systems engineers to keep up with innovation, these same technologies are also becoming an engineering group’s best friend.
IBM Engineering Lifecycle Management (ELM)
As we’ve already alluded to in other recent blogs, IBM created its ELM software as an end-to-end engineering lifecycle management solution to “help meet the stringent requirements of ASPICE.” As an infrastructure to manage the vehicle development ecosystem and all its complexities, the IBM ELM toolkit incorporates every phase of development, from design and review to testing and compliance.
To its credit, IBM worked closely with ASPICE assessors and practitioner committees to make sure IBM ELM enables automotive manufacturers and suppliers to address the standard’s complex regulations to achieve full compliance. Moreover, in their 2019–2020 decision matrix for ALM and DevOps solutions, industry analysts at Ovum rated IBM ELM as a market leader.
The upgraded version 7.0 of IBM ELM is due for release in early 2020.
IBM ELM Managed Services by ClearObject
In tandem with IBM’s ELM effort, IBM worked with ClearObject to create the cloud-based offering for IBM ELM Managed Services by ClearObject, which features a complete integration layer for ELM structured by IBM. With end-to-end integration between IBM ELM’s core engineering functions for Requirements, Test, Workflow Management, and Systems Design (MBSE), these managed services give engineering and development teams a hosted ELM-based quality requirements management configuration and solid architecture managed across the lifecycle of enterprise scale. (Teams can also take advantage of ClearObject’s expertise in IoT, AI and machine learning for their development environment.)
More importantly as Software as a Service (SaaS), entire engineering and development organizations can participate globally and evaluate developed concepts without having to dedicate their own IT resources.
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