Gorka Olalde

Abstract—The rapid increase in number of devices in Internet-of-Things generates astronomic amounts of data. Dealing with noisy and low quality data uses more effort than the data analysis itself. Dealing with noisy data at the source would significantly reduce the effort of pre-processing during analysis, as well as the storage and bandwidth overhead. In this paper we introduce an Adaptive Signal Processing Platform (ASPF) for CPS/IoT Ecosystems. It provides ability to dynamically detect noise variation in a signal and successfully filter these components out of the signal leaving only clean and useful data. The paper shows two approaches with different requirements on effort and scalability.

Abstract—One of the major challenges in the verification of complex industrial Cyber-Physical Systems is the difficulty of determining whether a particular system output or behaviour is correct or not, the socalled test oracle problem. Metamorphic testing alleviates the oracle problem by reasoning on the relations that are expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. In this paper, we present a case study aiming at automating this process. To this end, we implemented GAssertMRs, a tool to automatically generate MRs with genetic programming. We assess the cost-effectiveness of this tool in the context of an industrial case study from the elevation domain. Our experimental results show that in most cases GAssertMRs outperforms the other baselines, including manually generated MRs developed with the help of domain experts. We then describe the lessons learned from our experiments and we outline the future work for the adoption of this technique by industrial practitioners.

Abstract—Elevators are among the oldest and most widespread transportation systems, yet their complexity increases rapidly to satisfy customization demands and to meet quality of service requirements. Verification and validation tasks in this context are costly, since they rely on the manual intervention of domain experts at some points of the process. This is mainly due to the difficulty to assess whether the elevators behave as expected in the different test scenarios, the so-called test oracle problem. Metamorphic testing is a thriving testing technique that alleviates the oracle problem by reasoning on the relations among multiple executions of the system under test, the so-called metamorphic relations. In this practical experience paper, we report on the application of metamorphic testing to verify an industrial elevator dispatcher. Together with domain experts from the elevation sector, we defined multiple metamorphic relations that consider domain-specific quality of service measures. Evaluation results with seeded faults show that the approach is effective at detecting faults automatically.

Abstract—Cyber-Physical Systems (CPSs) are systems that integrate digital cyber computations with physical processes. The software embedded in CPSs has a long life-cycle, requiring constant evolution to support new requirements, bug fixes, and deal with hardware obsolescence. To date, the development of software for CPSs is fragmented, which makes it extremely expensive. This could be substantially enhanced by tightly connecting the development and operation phases, as is done in other software engineering domains (e.g., web engineering through DevOps). Nevertheless, there are still complex issues that make it difficult to use DevOps techniques in the CPS domain, such as those related to hardware-software co-design. To pave the way towards DevOps in the CPS domain, in this paper we instantiate part of the reference architecture presented in the H2020 Adeptness project, which is based on microservices that allow for the continuous deployment, monitoring and validation of CPSs. To this end, we elaborate a systematic methodology that considers as input both domain expertise and a previously defined taxonomy for DevOps in the CPS domain. We obtain a generic microservice template that can be used in any kind of CPS. In addition, we instantiate this architecture in the context of an industrial case study from the elevation domain.

Abstract—The abstract test cases generated through model-based testing (MBT) need to be concretized to make them executable on the software under test (SUT). Multiple researchers proposed different solutions, e.g., by utilizing adapters for concretization of abstract test cases and generation of test scripts. In this paper, we propose our Model-Based Test scrIpt GenEration fRamework (TIGER) based on GraphWalker, an open source MBT tool. The framework is capable of generating test scripts for embedded software controlling functions of a cyber physical system such as passenger trains developed at Bombardier Transportation AB. The framework follows some defined mapping rules for the concretization of abstract test cases. We have evaluated the generated test scripts using an industrial case study in terms of fault detection. We have induced faults in the model of the SUT based on three mutation operators to generate faulty test scripts. The aim of generating faulty test scripts is to produce failed test steps and to guarantee the absence of faults in the SUT. Moreover, we have also generated the test scripts using the correct version of the model and executed it to analyse the behaviour of the generated test scripts in comparison with manually-written test scripts. The results show that the test scripts generated by GW using the proposed framework are executable, provide 100% requirements coverage and can be used to uncover faults at software-in-the-loop simulation level of sub-system testing.

Abstract—Model-based testing (MBT) generates many test cases for validating a system under test against the user-defined requirements. Cloud computing provides powerful resources that can be utilised to execute these many test cases that would otherwise take much resources locally. Other benefits of utilizing cloud-based resources are elastic and on-demand, rapid provisioning and release of new, potentially value-adding services. Although cloud providers such as Amazon Web Services (AWS) have provided the necessary technologies for successful cloud- based operation, it remains difficult to migrate and hence achieve the realisation of MBT as a service for traditional in-house testing operations, especially for embedded software. In this paper, we present a series of cloud-based architectures powered by AWS and an open-source MBT tool, GraphWalker. These architectures are realized at simulation testing stage for real-world embedded software and particularly cater for online MBT, whereby the model-based tool is deployed as a RESTful web service, accessible through a number of REST API commands. The presented architectures as well as their realization through AWS can be adopted in future for more advanced levels of simulation testing of embedded software.

Abstract—The software of elevators requires maintenance over several years to deal with new functionality, correction of bugs or legislation changes. To automatically validate this software, test oracles are necessary. A typical approach in industry is to use regression oracles. These oracles have to execute the test input both, in the software version under test and in a previous software version. This practice has several issues when using simulation to
test elevators dispatching algorithms at system level. These issues include a long test execution time and the impossibility of re-using test oracles both at different test levels and in operation. To deal with these issues, we propose DARIO, a test oracle that relies on
regression learning algorithms to predict the Qualify of Service of the system. The regression learning algorithms of this oracle are trained by using data from previously tested versions. An empirical evaluation with an industrial case study demonstrates the feasibility of using our approach in practice. A total of five regression learning algorithms were validated, showing that the regression tree algorithm performed best. For the regression tree algorithm, the accuracy when predicting verdicts by DARIO ranged between 79 to 87%.

Remote software deployment and updating has long been common-
place in many different fields, but now, the increasing expansion of IoT and CPSoS (Cyber-Physcal System of Systems) has highlighted the need for additional mechanisms in these systems, to ensure the correct behaviour of the deployed software version after deployment. In this sense, this paper investigates the use of Machine Learning algorithms to predict acceptable behaviour in system performance of a new software release. By monitoring the real performance, eventual unexpected problems can be identified. Based on previous knowledge and actual run-time information, the proposed approach predicts the response time that can be considered acceptable for the new software release, and this information is used to identify problematic releases. The mechanism has been applied to the post-deployment monitoring of traffic algorithms in elevator systems. To evaluate the approach, we have used performance mutation testing, obtaining good results. This paper makes two contributions. First, it proposes several regression learners that have been trained with different types of traffic profiles to efficiently predict response time of the traffic dispatching algorithm. This prediction is then compared with the actual response time of the new algorithm release, and provides a verdict about its performance.Secondly, a comparison of the different learners is performed.

Cyber-Physical Systems (CPSs) are susceptible to various anomalies during their operations. Thus, it is important to detect such anomalies. Detecting such anomalies is challenging since it is uncertain when and where anomalies can happen. To this end, we present a novel approach called Anomaly deTection with digiTAl twIN (ATTAIN), which continuously and automatically builds a digital twin with live data obtained from a CPS for anomaly detection. ATTAIN builds a Timed Automaton Machine (TAM) as the digital representation of the CPS, and implements a Generative Adversarial Network (GAN) to detect anomalies. GAN uses a GCN-LSTM-based module as a generator, which can capture temporal and spatial characteristics of the input data and learn to produce realistic unlabeled fake samples. TAM labels these fake samples, which are then fed into a discriminator along with real labeled samples. After training, the discriminator is capable of distinguishing anomalous data from normal data with a high F1 score. To evaluate our approach, we used three publicly available datasets collected from three CPS testbeds. Evaluation results show that ATTAIN improved the performance of two state-of-art anomaly detection methods by 2.413%, 8.487% and 5.438% on average on the three datasets, respectively. Moreover, ATTAIN achieved on average 8.39% increase in the anomaly detection capability with digital twins as compared with an approach of not using digital twins.