SC Harvester Papers Database Interface

Co-evolution of simulink models in a model-based product line

R. Jongeling, A. Cicchetti, Federico Ciccozzi, Jan Carlson. In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems. 2020

Abstract: Co-evolution of metamodels and conforming models is a known challenge in model-driven engineering. A variation of co-evolution occurs in model-based software product line engineering, where it is needed to efficiently co-evolve various products together with the single common platform from which they are derived. In this paper, we aim to alleviate manual efforts during this co-evolution process in...

Using Benji to systematically evaluate model comparison algorithms

Lorenzo Addazi, A. Cicchetti. In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. 2020

Abstract: Model comparison is a critical task in model-driven engineering. Its correctness enables an effective management of model evolution, synchronisation, and even other tasks, such as model transformation testing. The literature is rich as concerns comparison algorithms approaches, however the same cannot be said for their systematic evaluation. In this paper we present Benji, a tool for the generatio...

Towards dynamic safety assurance for Industry 4.0

M. Javed, Faiz ul Muram, H. Hansson, S. Punnekkat, Henrik Thane. In: J. Syst. Archit.. 2020

Abstract: Abstract The goal of Industry 4.0 is to be faster, more efficient and more customer-centric, by enhancing the automation and digitalisation of production systems. Frequently, the production in Industry 4.0 is categorized as safety-critical, for example, due to the interactions between autonomous machines and hazardous substances that can result in human injury or death, damage to machines, propert...

Magnifier: A Compositional Analysis Approach for Autonomous Traffic Control

Maryam Bagheri, M. Sirjani, Ehsan Khamespanah, Christine Baier, A. Movaghar. In: IEEE Transactions on Software Engineering. 2020

Abstract: Autonomous traffic control systems are large-scale systems with critical goals. To satisfy expected properties, these systems adapt themselves to possible changes in their environment and in the system itself. The adaptation may result in further changes propagated throughout the system. For each change and its consequent adaptation, assuring the satisfaction of properties of the system at runtime...

Magnifier: A Compositional Analysis Approach for Autonomous Traffic Control

Maryam Bagheri, M. Sirjani, Ehsan Khamespanah, Christine Baier, A. Movaghar. In: IEEE Transactions on Software Engineering. 2020

Abstract: Autonomous traffic control systems are large-scale systems with critical goals. To satisfy expected properties, these systems adapt themselves to possible changes in their environment and in the system itself. The adaptation may result in further changes propagated throughout the system. For each change and its consequent adaptation, assuring the satisfaction of properties of the system at runtime...

Statistical Models for the Analysis of Optimization Algorithms With Benchmark Functions

D. I. Mattos, J. Bosch, H. Olsson. In: IEEE Transactions on Evolutionary Computation. 2020

Abstract: Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for familywise errors in multiple group comparisons, among several other problems. Bayesian data analysis (BDA) addresses many of t...

ACM SIGSOFT Empirical Standards

P. Ralph, Sebastian Baltes, D. Bianculli, Y. Dittrich, M. Felderer et al. In: ArXiv. 2020

Abstract: Empirical Standards are brief public document that communicate expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for common research methods in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, c...

Defects4J as a Challenge Case for the Search-Based Software Engineering Community

Gregory Gay, René Just. In: . 2020

Understanding The Impact of Solver Choice in Model-Based Test Generation

Meng, Gregory Gay. In: Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). 2020

Abstract: Background: In model-based test generation, SMT solvers explore the state-space of the model in search of violations of specified properties. If the solver finds that a predicate can be violated, it produces a partial test specification demonstrating the violation. Aims: The choice of solvers is important, as each may produce differing counterexamples. We aim to understand how solver choice impact...

Activity recognition and user preference learning for automated configuration of IoT environments

Fahed Alkhabbas, Sadi Alawadi, Romina Spalazzese, P. Davidsson. In: Proceedings of the 10th International Conference on the Internet of Things. 2020

Abstract: Internet of Things (IoT) environments encompass different types of devices and objects that offer a wide range of services. The dynamicity and uncertainty of those environments, including the mobility of users and devices, make it hard to foresee at design time available devices, objects, and services. For the users to benefit from such environments, they should be proposed services that are relev...

Analyzing Distributed Deep Neural Network Deployment on Edge and Cloud Nodes in IoT Systems

M. Ashouri, F. Lorig, P. Davidsson, Romina Spalazzese, Sergej Svorobej. In: 2020 IEEE International Conference on Edge Computing (EDGE). 2020

Abstract: For the efficient execution of Deep Neural Networks (DNN) in the Internet of Things, computation tasks can be distributed and deployed on edge nodes. In contrast to deploying all computation to the cloud, the use of Distributed DNN (DDNN) often results in a reduced amount of data that is sent through the network and thus might increase the overall performance of the system. However, finding an app...

Keywords-based test categorization for Extra-Functional Properties

Muhammad Abbas, A. Rauf, Mehrdad Saadatmand, Eduard Paul Enoiu, Daniel Sundmark. In: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). 2020

Abstract: Categorizing existing test specifications can provide insights on coverage of the test suite to extra-functional properties. Manual approaches for test categorization can be time-consuming and prone to error. In this short paper, we propose a semi-automated approach for semantic keywords-based textual test categorization for extra-functional properties. The approach is the first step towards cover...

Poster: Performance Testing Driven by Reinforcement Learning

M. H. Moghadam, Mehrdad Saadatmand, Markus Borg, M. Bohlin, B. Lisper. In: 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). 2020

Abstract: Performance testing remains a challenge, particularly for complex systems. Different application-, platform- and workload-based factors can influence the performance of software under test. Common approaches for generating platform- and workload-based test conditions are often based on system model or source code analysis, real usage modeling and use-case based design techniques. Nonetheless, crea...

Learning How to Search: Generating Exception-Triggering Tests Through Adaptive Fitness Function Selection

H. Almulla, Gregory Gay. In: 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). 2020

Abstract: Search-based test generation is guided by feedback from one or more fitness functions—scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals—such as forcing the class-under-test to throw exceptions— do not have a known fitness function formulation. We propose that meeting such goals requires tr...

Checkable Safety Cases: Enabling Automated Consistency Checks between Safety Work Products

Carmen Cârlan, D. Petrisor, B. Gallina, Hannes Schoenhaar. In: 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). 2020

Abstract: In the automotive domain, the employment of agile development is currently hindered by the fact that the safety lifecycle, which implies the creation and maintenance of safety work products, is manually executed, being a complex and expensive process. Given a change in the system under consideration, ISO 26262 recommends that the impact of that change on the safety case of the system shall be asse...