Parameter Optimization in Control Software using Statistical Fault Localization Techniques

TL;DR

This paper introduces a statistical fault localization method for control software, specifically targeting parameter tuning in look-up maps of embedded controllers, to identify causes of sub-optimal performance.

Contribution

It extends software fault localization techniques to continuous parameters in reactive control systems, providing a practical simulation-based framework for automotive control tuning.

Findings

Accurately identified faulty parameters in industrial case studies

Effective in ranking parameters impacting system performance

Applicable to complex, real-world control systems

Abstract

Embedded controllers for cyber-physical systems are often parameterized by look-up maps representing discretizations of continuous functions on metric spaces. For example, a non-linear control action may be represented as a table of pre-computed values, and the output action of the controller for a given input is computed by using interpolation. For industrial-scale control systems, several man-hours of effort is spent in tuning the values within the look-up maps, and sub-optimal performance is often associated with inappropriate values in 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 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 automotive 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.