A Heuristic Approach based on Prioritized Quality Attributes for the Evolutionary Architecting of Automotive Embedded Systems
Abstract: Over the years, vehicles have become very complex systems involving multiple domains of engineering. This is mainly due to the advent of electronics and its use in the automotive industry. Electronically controlled systems offer much more flexibility and reliability compared to conventional mechanical systems. This has also led to a complicated system architecture including many electronic control units, robust communication systems and an intricate wiring harness. Development of such highly complex systems is an arduous task. In most cases, development is done incrementally rather than a complete overhaul. This kind of development process is called an evolutionary architecting process (EAP). Two architecting methodologies, Architecture Trade-Off Analysis Method and Cost Benefit Analysis Method, were chosen to study EAP further in an industrial context. Based on these two methodologies, a lightweight framework for architectural decisionmaking (LFAD) is proposed to align with automotive-specific needs. This thesis verifies LFAD with a real industrial case in Scania CV AB. The LFAD method provides a swift way to assimilate available information regarding a project by narrowing down different aspects to a known set of quality attributes and organise them in a useful way from an architectural perspective. Interviews were chosen as a primary method to conduct this study and they were conducted at different automotive firms like Scania, Volvo Trucks, Volvo Cars and Einride. The interviews provided inputs to the method studies and in identifying the quality attributes that are usually considered for EAP. The resulting quality attributes are visualised as a utility tree along with its description. The thesis also delves into analysing the bias involved in EAP with the involvement of multiple engineering disciplines. Inputs were taken from the interviews as well. From the study, it can be seen that different organizational characteristics affect the decision-making process. For example, the knowledge about different systems accumulated over the years are hugely dependent on how they are organized as groups. There is also a need for homogeneity in semantics among these groups when dealing with complex multi-disciplinary systems. And finally, it is concluded that bias does play a major role in the decision-making process and must be addressed on a case by case basis.
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