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Parameter Optimization in Control Software using Statistical Fault Localization Techniques by Deshmukh, Jyotirmoy V.; Jin, Xiaoqing; Majumdar, Rupak; Prabhu, Vinayak S. is a scholarly article available to read on EtoBox.

What is Parameter Optimization in Control Software using Statistical Fault Localization Techniques about?

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

Author

Deshmukh, Jyotirmoy V.; Jin, Xiaoqing; Majumdar, Rupak; Prabhu, Vinayak S.

Published

2017

Language

EN