Complexity Engineering Uncategorized

Complexity of Car Electronics: Testing and Reliability

Circuit board with electronic components

In an interesting article entitled

Reliability Paradox for Worldwide Automotive Electronics

and published in: 2017 Annual Reliability and Maintainability Symposium (RAMS), one may read:

“Automotive Electronics is growing ever since the technological advancement has brought about a revolution in the Automotive Semiconductor and Telematics industry, especially in the past decade. Utilization of microelectronics and hence circuits’ complexity is increasing continuously in automotive industry. Advancement in electronic technology increases the dominance of electronics in automotive markets, and reliability requirements for Automobile Electronics have grown over the past many years. However, the faults and rate of defects is also increasing as indicated by proportion of number of recalls made by large automobile companies, and this poses a difficult `Paradox’ to understand and curb. One of the reasons is that the increase in technological advancements makes the vehicles’ system more complex and difficult to test for reliability. Also, operating parameters such as temperature, humidity, etc. are increasingly stringent with the continuous development of the electronic systems in vehicles. Short time to market and ineffective design for reliability methodology employed, including various misconceptions on reliability testing, have rendered a lag in providing components and systems that match the increasing reliability requirements for vehicles. Therefore, a call for greater effort in reliability understanding and systematic design for reliability methodology is essential if continuous engagement of advanced electronic technologies is to be continued. The transition from Mechanical Automotive system to Electronics Automotive system and its effect on the Automotive industry is discussed in this work. The fact that technological benefits are not helping in improving reliability of the Automotive Electronics system is explained by studying the worldwide automotive recalls and the Paradox of Automotive Electronics Reliability is presented and explained.”

How can QCM technology from Ontonix help? There are essentially two roads:

  1. Improve SW testing procedures.
  2. Monitor SW/electronics complexity in real-time and provide early-warnings of problems, indicating their sources.

As the complexity of a piece of SW increases so does the complexity of its testing procedures. In order to reduce the number of tests, or to make the testing campaign more thorough and deep with the same number of tests, one can increase the complexity of the test vectors {x}. Suppose that a set of N test vectors, each of length M, is created. These can be arranged in a rectangular M by N array [X] = [{x1},….,{xN}]. If the complexity C([X]) of this array can be maximized, it will yield a deeper test set, putting the SW in question under more “pressure”. This will, ultimately, impact reliability.

As far as monitoring of car SW/electronics complexity in real-time, there exist hardware solutions that have been tested in civilian and military contexts.

The bottom line: if you want to combat and contrast complexity, you need to measure complexity.  Simple but not always obvious. Oh, and if someone claims that he is managing complexity without actually measuring it, it is only marketing. And a flat lie.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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