Author:
Enoiu Eduard Paul,Seceleanu Cristina
Abstract
Nowadays, embedded systems are increasingly complex, meaning that traditional testing methods are costly to use and infeasible to directly apply due to the complex interactions between hardware and software. Modern embedded systems are also demanded to function based on low-energy computing. Hence, testing the energy usage is increasingly important. Artifacts produced during the development of embedded systems, such as architectural descriptions, are beneficial abstractions of the system’s complex structure and behavior. Electronic Architecture and Software Tools Architecture Description Language (EAST-ADL) is one such example of a domain-specific architectural language targeting the automotive industry. In this paper, we propose a method for testing design models using EAST-ADL architecture mutations. We show how fault-based testing can be used to generate, execute and select tests using energy-aware mutants—syntactic changes in the architectural description, used to mimic naturally occurring energy faults. Our goal is to improve testing of complex embedded systems by moving the testing bulk from the actual systems to models of their behaviors and non-functional requirements. We combine statistical model-checking, increasingly used in quality assurance of embedded systems, with EAST-ADL architectural models and mutation testing to drive the search for faults. We show the results of applying this method on an industrial-sized system developed by Volvo GTT. The results indicate that model testing of EAST-ADL architectural models can reduce testing complexity by bringing early and cost-effective automation.
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Engineering (miscellaneous)
Cited by
2 articles.
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1. Test Generation and Mutation Analysis of Energy Consumption using UPPAAL SMC and MATS;2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW);2023-04
2. An Industrial Software Model Checking Method Based on Machine Learning and Its Application in Education;Application of Big Data, Blockchain, and Internet of Things for Education Informatization;2023