Application of Abstract Interpretation to the Automotive Electronic Control System
The verification and validation of industrial automotive systems is increasingly challenging as they become larger and more complex. Recent automotive Electric Control Units (ECUs) have approximately one half to one million of lines of code, and a modern automobile can contain hundreds of controllers. Significant work-hours are needed to understand and manage systems of this level of complexity. One particular challenge is understanding the changes to the software across development phases and revisions. To this end, we present a code dependency analysis tool that enhances designer understanding. It combines abstract interpretation and graph based data analysis to generate visualized dependency graphs on demand to support designer’s understanding of the code. We demonstrate its value by presenting dependency graph visuals for an industrial application, and report results showing significant reduction of work-hours and enhancement of the ability to understand the software.
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