With the advancements in Internet of Things (IoT) and innovations in the networking domain, Cyber-Physical Systems (CPS) are rapidly adopted in various domains from autonomous vehicles to manufacturing systems to improve the efficiency of the overall development of complex physical systems. CPS models allow an easy and cost-effective approach to alter the architecture of the system that yields optimal performance. This is especially crucial in the early stages of development of a physical system. Developing effective testing strategies to test CPS models is necessary to ensure that there are no defects during the execution of the system. Typically, a set of requirements are defined from the domain expertise to assert the system's behavior on different possible inputs. To effectively test CPS, a large number of test inputs is required to observe their performance on a variety of test inputs. But real-world CPS models are compute-intensive (i.e. takes a significant amount of time to execute the CPS for a given test input). Therefore, it is almost impossible to execute CPS models over a large number of test inputs. This leads to sub-optimal fixes based on the identified defects which may lead to costly issues at later stages of development.
In this thesis, we aim to improve the efficiency of existing search-based software testing approaches to test compute-intensive CPS by combining them with ML. We call these ML-assisted test generation. In this work, we investigate two alternate ML-assisted test generation techniques: (1) surrogate-assisted and (2) ML-guided test generation, to efficiently test a given CPS model. Both the surrogate-assisted and ML-guided test generation can generate many test inputs. Therefore, we propose to build failure models that generate explainable rules on failure-inducing test inputs of the CPS model. Surrogate-assisted test generation involves using ML as a replacement to CPS under test so that the fitness value of some test inputs are predicted rather than executing them using CPS. A large number of test inputs are generated by combining cheap surrogate predictions and compute-intensive execution of CPS model to find the labels of the test inputs. Specifically, we propose a new surrogate-assisted test generation technique that leverages multiple surrogate models simultaneously and dynamically selects the prediction from the most accurate label. Alternatively, ML-assisted test generation aims to estimate the boundary regions that separate test inputs that pass the requirements and test inputs that fail the requirements and subsequently guide the sampling of test inputs from these boundary regions. Further, the test data generated by the ML-assisted test generation techniques are used to infer two alternative failure models namely the Decision Rule Model (DRM) and Decision Tree Model (DTM) that characterizes the failure circumstances of the CPS model. We conduct an empirical evaluation of the accuracy of failure models inferred from test data generated by both ML-assisted test generation techniques. Using a total of 15 different functional requirements from 5 Simulink-based benchmarks CPS, we observed that the proposed dynamic surrogate-assisted test generation technique generates failure models with an average accuracy of 83% for DRM and 90% for DTM. The average accuracy of the dynamic surrogate-assisted technique has a 16.9% improvement in the average accuracy of DRM and a 7.1% improvement in the average accuracy of DTM compared to the random search baseline.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/45155 |
| Date | 17 July 2023 |
| Creators | Chandar, Abhishek |
| Contributors | Sabetzadeh, Mehrdad, Nejati, Shiva |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
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