Parameter Optimization in Control Software using Statistical Fault Localization Techniques

Embedded controllers for cyber-physical systems are often parameterized by look-up maps representing discretizations of continuous functions on metric spaces. For example, a nonlinear control action may be represented as a table of precomputed values, and the output action of the controller for a given input computed by using interpolation. For industrialscale control systems, several man-hours of effort are spent in tuning the values within the look-up maps. Suppose that during testing, the controller code is found to have sub-optimal performance. The parameter fault localization problem asks which parameter values in the code are potential causes of the sub-optimal behavior. We present a statistical parameter fault localization approach based on binary similarity coefficients and set spectra methods. Our approach extends previous work on (traditional) software fault localization to a quantitative setting where the parameters encode continuous functions over a metric space and the program is reactive. We have implemented our approach in a simulation workflow for control systems in Simulink. Given controller code with parameters (including look-up maps), our framework bootstraps the simulation workflow to return a ranked list of map entries which are deemed to have most impact on the performance. On a suite of industrial case studies with seeded errors, our tool was able to precisely identify the location of the errors.