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.
Authors: Wasif Afzal, Amirali Piadehbasmenj
Title of the source: 9th International Conference on Cyber-Physical Systems and Internet of Things
Year: 2021More info
Using Machine Learning to Build Test Oracles: an Industrial Case Study on Elevators Dispatching algorithms
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%.
Authors: Aitor Arrieta, Jon Ayerdi, Miren Illarramendi, Aitor Agirre, Goiuria Sagardui, Maite Arratibel
Title of the source: 2nd ACM/IEEE International Conference on Automation of Software Tests
Using Regression Learners to Predict Performance Problems on Software Updates: a Case Study on Elevators Dispatching Algorithms
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.
Authors: Aitor Gartziandia, Aitor Arrieta, Aitor Agirre, Goiuria Sagardui, Maite Arratibel
Title of the source: Proceedings of the 36th Annual ACM Symposium on Applied Computing
Relevant pages: 135-144
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.
Authors: Qinghua Xu, Shaukat Ali, Tao Yue
Title of the source: IEEE International Conference on Software Testing
An Evaluation of Monte Carlo-Based Hyper-Heuristic for Interaction Testing of Industrial Embedded Software Applications
Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the exponential Monte Carlo with counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t-wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which is used for developing a train control management system at Bombardier Transportation Sweden AB. The results show that Q-EMCQ is an efficient technique for test case generation. Additionally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach. The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyper-heuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time.
Authors: Bestoun S. Ahmed, Eduard Enoiu, Wasif Afzal, Kamal Z. Zamli
Title of the source: Journal of soft computing
Relevant pages: 13929-13954
Software systems that are embedded in autonomous Cyber-Physical Systems (CPSs) usually have a large life-cycle, both during its development and in maintenance. This software evolves during its life-cycle in order to incorporate new requirements, bug fixes, and to deal with hardware obsolescence. The current process for developing and maintaining this software is very fragmented, which makes developing new software versions and deploying them in the CPSs extremely expensive. In other domains, such as web engineering, the phases of development and operation are tightly connected, making it possible to easily perform software updates of the system, and to obtain operational data that can be analyzed by engineers at development time. However, in spite of the rise of new communication technologies (e.g., 5G) providing an opportunity to acquire Design-Operation Continuum Engineering methods in the context of CPSs, there are still many complex issues that need to be addressed, such as the ones related with hardware-software co-design. Therefore, the process of Design-Operation Continuum Engineering for CPSs requires substantial changes with respect to the current fragmented software development process. In this paper, we build a taxonomy for Design-Operation Continuum Engineering of CPSs based on case studies from two different industrial domains involving CPSs (elevation and railway). This taxonomy is later used to elicit requirements from these two case studies in order to present a blueprint on adopting Design-Operation Continuum Engineering in any organization developing CPSs.
Authors: Jon Ayerdi , Aitor Garciandia , Aitor Arrieta , Wasif Afzal, Eduard Paul Enoiu, Aitor Agirre , Goiuria Sagardui , Maite Arratibel , Ola Sellin
Title of the source: 28th International Requirements Engineering Conference
Relevant pages: 280-290