Search Based Model in the Loop Testing for Cyber Physical Systems

Ţurlea, Ana

doi:10.1109/euc.2018.00011

Cited by 4 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Lochau and Goltz [72] (2010)mentioning

confidence: 99%

“…Turlea [82] (2018): Problem: Increasing the safety of software controlled complex systems, such as automotive vehicles that use multiple, distributed electronic control units to control the safe operation of engines, brakes and airbags, requires extensive testing, which can be time consuming and expensive. Approach: Turlea [82] presents a discrete genetic algorithm to automate the generation of test cases that indicate potential violations of the requirements at the MiL level using simulationmodels of a CPS. As objectives, the approach uses four functions, computeReachabilityT, computeMaxOvershoot, computeStabilisationT and com-puteMaxSlipInterval, to calculate the metrics, which seem to be very specific to the subject of the study (which is a cruise control system for an e-Bike system).…”

Section: Lochau and Goltz [72] (2010)mentioning

confidence: 99%

“…The author implemented a prototype of the approach in Python. Results: Turlea [82] evaluates their approach by performing three experiments. The results show that the approach automatically generates several test cases that indicate potential violations of the requirements at the MiL level, which can be further investigated by evaluating the test cases at the hardware-in-the-loop level.…”

Section: Lochau and Goltz [72] (2010)mentioning

confidence: 99%

See 2 more Smart Citations

Survey on test case generation, selection and prioritization for cyber‐physical systems

Sadri-Moshkenani

Bradley

Rothermel

2021

Software Testing Verif & Rel

View full text Add to dashboard Cite

A cyber-physical system (CPS) is a collection of computing devices that communicate with each other, operate in the target environment via actuators and interact with the physical world through sensors in a feedback loop. CPSs need to be safe and reliable and function in accordance with their requirements. Testing, focusing on a CPS model and/or its code, is the primary approach used by engineers to achieve this. Generating, selecting and prioritizing test cases that can reveal faults in CPSs, from the wide range of possible input values and stimuli that affect their operation, are of central importance in this process. To date, however, in our search of the literature, we have found no comprehensive survey of research on test case generation, selection and prioritization for CPSs. In this article, therefore, we report the results of a survey of approaches for generating, selecting and prioritizing test cases for CPSs; the results illustrate the progress that has been made on these approaches to date, the properties that characterize the approaches and the challenges that remain open in these areas of research.

show abstract

Section: Lochau and Goltz [72] (2010)mentioning

confidence: 99%

Section: Lochau and Goltz [72] (2010)mentioning

confidence: 99%

Section: Lochau and Goltz [72] (2010)mentioning

confidence: 99%

See 1 more Smart Citation

Survey on test case generation, selection and prioritization for cyber‐physical systems

Sadri-Moshkenani

Bradley

Rothermel

2021

Software Testing Verif & Rel

View full text Add to dashboard Cite

show abstract

“…The input search space of such systems is substantial, therefore metaheuristics and random search based techniques, are often used to generate the test cases [1]. Further, the system model is used to execute the test cases, as it is unpractical to use the physical system, especially on the pre-deployment testing stage [2].…”

Section: Introductionmentioning

confidence: 99%

“…Another direction of research is automatic search based generation of test suites for CPS. Those approaches are mostly often focused on finding the test cases with the best requirements coverage and diversity, but not falsification [1], [4], [5]. They lack flexibility as often require an external software to generate the initial test cases.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Testing of Cyber Physical Systems

Humeniuk¹,

Antoniol²,

Khomh³

2021

Preprint

View full text Add to dashboard Cite

Consumer grade cyber-physical systems are becoming an integral part of our life, automatizing and simplifying everyday tasks; they are almost always capable of connection to the network allowing remote monitoring and programming. They rely on powerful programming languages, cloud infrastructures, and ultimately on complex software stacks. Indeed, due to complex interactions between hardware, networking and software, developing and testing such systems is known to be a challenging task. Ensuring properties such as dependability, security or data confidentiality is far from obvious. Various quality assurance and testing strategies have been proposed.The most common approach for pre-deployment testing is to model the system and run simulations with models or software in the loop. In practice, most often, tests are run for a small number of simulations, which are selected based on the engineers' domain knowledge and experience.To improve quality assurance practices, previous works such as the ARISTEO paper, promulgate the adoption of searchbased techniques and models to falsify a given property. Our conjecture is that effectiveness and usefulness are improved if the environment is also explicitly accounted for. In the approach presented in this paper, we seek properties violations via simulation and search based-techniques. However, the environment, more precisely, a set of environment conditions are accounted for via models learned on real world data. Learned models, represent the interactions of cyber-physical systems and the surroundings under specific environmental conditions. Models are then fed into a combined simulation -search algorithms. Indeed, we seek to find a model combination (i.e., environment realization) and sequence of system actions violating a particular property.We have implemented our approach in Python, using standard frameworks and used it to generate scenarios violating temperature constraints for a smart thermostat implemented as a part of our IoT testbed. Data collected from an application managing a smart building have been used to learn models of the environment under ever changing conditions. The suggested approach allowed us to identify several pit-fails, scenarios (i.e., environment conditions), where the system behaves not as expected.

show abstract

Machine learning‐based test oracles for performance testing of cyber‐physical systems: An industrial case study on elevators dispatching algorithms

Gartziandia

Arrieta

Ayerdi

et al. 2022

J Software Evolu Process

View full text Add to dashboard Cite

The software of systems of elevators needs constant maintenance to deal with new functionality, bug fixes, or legislation changes. To automatically validate the software of these systems, a typical approach in industry is to use regression oracles, which execute test inputs both in the software version under test and in a previous software version. However, these practices require a long test execution time and cannot be re‐used at different test phases. To deal with these issues, we propose Dispatching AlgoRIthm Oracle (DARIO), a test oracle that relies on regression machine‐learning algorithms to detect both functional and non‐functional problems of the system. The machine‐learning algorithms of this oracle are trained by using data from previously tested versions to predict reference functional and non‐functional performance values of the new versions. An empirical evaluation with an industrial case study demonstrates the feasibility of using our approach. A total of five regression learning algorithms were validated by using mutation testing techniques. For the context of functional bugs, the accuracy when predicting verdicts by DARIO ranged between 95% and 98%, across the different scenarios proposed. For the context of non‐functional bugs, were competitive too, having an accuracy when predicting verdicts by DARIO ranged between 83% and 87%.

show abstract

Search Based Model in the Loop Testing for Cyber Physical Systems

Cited by 4 publications

References 20 publications

Survey on test case generation, selection and prioritization for cyber‐physical systems

Survey on test case generation, selection and prioritization for cyber‐physical systems

Data Driven Testing of Cyber Physical Systems

Machine learning‐based test oracles for performance testing of cyber‐physical systems: An industrial case study on elevators dispatching algorithms

Contact Info

Product

Resources

About