Spectrum-based Fault Localization in Embedded Software

Spectrum-based fault localization (SFL) shortens the test-diagnose-repair cycle by reducing the debugging effort. As a light-weight automated diagnosis technique it can easily be integrated with existing testing schemes. Since SFL is based on discovering statistical coincidences between system failures and the activity of the different parts of a system, its diagnostic accuracy is inherently limited. Using a common benchmark consisting of the Siemens set and the space program, we investigate this diagnostic accuracy as a function of several parameters (such as quality and quantity of the program spectra collected during the execution of the system), some of which directly relate to test design. Our results indicate that the superior performance the Ochiai similarity coefficient, taken from the molecular biology domain and introduced by us in the context of fault localization, is largely independent of test design. Furthermore, near-maximal diagnostic accuracy (exonerating over 80% of the blocks of code on average) is already obtained for low-quality error observations and limited numbers of test cases. The influence of the number of test cases is of primary importance for continuous (embedded) processing applications, where only limited observation horizons can be maintained.