A Novel Indirect Methodology Based on Execution Traces for Grading Functional Test Programs

Developing functional test programs for hardware testing is time-consuming and experience-wise. A functional test program’s quality is usually assessed only through expensive fault simulation campaigns during early development. This paper presents indirect quality measurements of fault detection capabilities of functional test programs to reduce the total cost of fault simulation in the early development stages. We present a methodology that analyzes the instruction trace generated by running functional test programs on-chip and building its control and dataflow graph. We use the graph to identify potential flaws that affect the program’s fault detection capabilities. We present different graph-based techniques to measure the programs’ quality indirectly. By exploiting standard debugging formats, we individuate instructions in the source code that affect the graph-based measurements. We perform experiments on an automotive device manufactured by STMicroelectronics, running functional test programs of different natures. Our results show that our metric allows test engineers to develop better functional test programs without basing their development solely on fault simulation campaigns.