Automated Software Defect Detection and Identification in Vehicular Embedded Systems

Abstract

Trends in the automotive industry confirm that the demand for testing of embedded systems, especially advanced driver assistance systems (ADAS), will grow dramatically in the near future. This paper proposes a new solution that automates the detection of software defects in embedded systems. The solution consists of a data-driven sampling algorithm to intelligently sample the testing space by sequentially generating test cases. Moreover, it segregates different defects from each other and identifies the signals that trigger each. The results are compared against other automated methods for defect identification and analysis, and it is found that this novel solution is able to identify defects more rapidly. In addition, it correctly separates defects and reliably reproduces each distinct defect.