Software Ecosystem (SECO) has emerged as a crucial concept, which represents a collaborative and interconnected environment in which a variety of actors engage in developing software systems. SECOs play a key role in the development of Cyber-Physical Systems (CPSs), that present a myriad of challenges, primarily due to the need for real-time responsiveness, reliability, security, and interoperability. The implications of leveraging SECOs for developing CPSs are profound in both research and practice. This paper aims to understand the collaboration between industry and academia within SECOs for the development of CPSs, identifying potential challenges and providing insights and guidelines for the proper management of these collaborations. We conducted a systematic literature review (SLR), complemented by empirical evidence collected through an opinion survey administered to the partners of the European collaborative project AIDOaRt, a concrete example of a SECO, which worked on the development of CPSs. From these findings we discuss the identified challenges, and potential effects on collaboration, in addition to our lessons learned in the AIDOaRt project and SECO.

Context: The increase in Internet of Things (IoT) devices gives rise to an increase in deceptive manipulations by malicious actors. These actors should be prevented from targeting the IoT networks. Cybersecurity threats have evolved and become dynamically sophisticated, such that they could exploit any vulnerability found in IoT networks. However, with the introduction of the Software Defined Network (SDN) in the IoT networks as the central monitoring unit, IoT networks are less vulnerable and less prone to threats. Objective: To present a comprehensive and unbiased overview of the state-of-the-art on IoT networks security enhancement using SDN controllers. Method: We review the current body of knowledge on enhancing the security of IoT networks using SDN with a Systematic Mapping Study (SMS) following the established guidelines. Results: The SMS result comprises 33 primary studies analyzed against four major research questions. The SMS highlights current research trends and identifies gaps in the SDN-IoT network security. Conclusion: We conclude that the SDN controller architecture commonly used for securing IoT networks is the centralized controller architecture. However, this architecture is not without its limitations. Additionally, the predominant technique utilized for risk mitigation is machine learning.

Context: To effectively defend against ever-evolving cybersecurity threats, software systems should be made as secure as possible. To achieve this, software developers should understand potential vulnerabilities and apply secure coding practices. To prepare these skilled professionals, it is important that cybersecurity concepts are included in programming courses taught at universities. Objective: To present a comprehensive and unbiased literature review on teaching of cybersecurity concepts in programming courses taught at universities. Method: We perform a Systematic Mapping Study. We present six research questions, define our selection criteria, and develop a classification scheme. Results and Conclusions: We select 24 publications. Our results show a wide range of research contributions. We also outline guidelines and identify opportunities for future studies. The guidelines include coverage of security knowledge categories and evaluation of contributions. We suggest that future studies should cover security issues, negative impacts, and countermeasures, as well as apply evaluation techniques that examine students' knowledge. The opportunities for future studies are related to advanced courses, security knowledge frameworks, and programming environments. Furthermore, there is a need of a holistic security framework that covers the security concepts identified in this study and is suitable for education.

Background: The edge computing paradigm allows to reduce latency and response time of applications by bringing computations and data storage closer to the locations where they are needed. Edge computing is used in different kinds of Internet of Things (IoT) applications. Maritime represents an important application domain for IoT applications and edge computing solutions. Modern vessels employ many different types of sensors, which produce a massive amount of data. Edge computing allows to perform computations and data analyses on-board a vessel or at the edge of the network. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on edge computing approaches for maritime applications. Method: A Systematic Mapping Study (SMS) of the existing edge computing approaches for maritime applications. Results: A taxonomy of 17 papers on edge computing approaches for maritime applications. Conclusion: The results of the study show that there is a small number of existing edge computing approaches for maritime applications. Most of the existing approaches focus mainly on monitoring and communication functions in vessels. Moreover, several research gaps exist with respect to the types of edge computing approaches, the purposes of using edge computing on vessels, and the data analysis techniques used for edge computing on vessels.

In this paper, we present the MegaM@Rt2 ECSEL project and discuss in details our approach for fostering collaboration in this project. We choose to use an internal hackathon approach that focuses on technical collaboration between case study owners and tool/method providers. The novelty of the approach is that we organize the technical workshop at our regular project progress meetings as a challenge-based contest involving all partners in the project. Case study partners submit their challenges related to the project goals and their use cases in advance. These challenges are concise enough to be experimented within approximately 4 hours. Teams are then formed to address those challenges. The teams include tool/method providers, case study owners and researchers/developers from other consortium members. On the hackathon day, partners work together to come with results addressing the challenges that are both interesting to encourage collaboration and convincing to continue further deeper investigations. Obtained results demonstrate that the hackathon approach stimulated knowledge exchanges among project partners and triggered new collaborations, notably between tool providers and use case owners.

Performance bottlenecks resulting in high response times and low throughput of software systems can ruin the reputation of the companies that rely on them. Almost two-thirds of performance bottlenecks are triggered on specific input values. However, finding the input values for performance test cases that can identify performance bottlenecks in a large-scale complex system within a reasonable amount of time is a cumbersome, cost-intensive, and time-consuming task. The reason is that there can be numerous combinations of test input values to explore in a limited amount of time. This paper presents PerfXRL, a novel approach for finding those combinations of input values that can reveal performance bottlenecks in the system under test. Our approach uses reinforcement learning to explore a large input space comprising combinations of input values and to learn to focus on those areas of the input space which trigger performance bottlenecks. The experimental results show that PerfxRL can detect 72% more performance bottlenecks than random testing by only exploring the 25% of the input space.

The Foundational Subset for Executable UML Models (fUML) and the Action language for fUML (Alf) can be used for creating executable models in the Eclipse-based UML editing tool called Papyrus. An fUML execution engine in Papyrus, such as Moka, allows to simulate or execute fUML models along with their associated Alf code. However, for exhaustive simulation of such models, one must provide input data required to reach and cover all important elements not only in the graphical fUML models, but also in the textual Alf code. In this paper, we present MATERA2-AlfTester, an Eclipse-plugin for exhaustive simulation and test generation for fUML models. MATERA2-AlfTester integrates with Papyrus and Moka tools and extends their functionally by allowing one to automatically generate test data, test suite with test oracle, and partial Java code at design time. We also present the simulation and testing process of MATERA2-AlfTester with the help of an example and demonstrate how exhaustive simulation and test generation with MATERA2-AlfTester can help designers in assessing and improving the quality of fUML models.

Despite the growing popularity of swarm-based applications of drones, there is still a lack of approaches to maximize the safety of swarms of drones by minimizing the risks of drone collisions. In this paper, we present an approach that uses optimization, learning, and automatic immediate responses (reflexes) of drones to ensure safe operations of swarms of drones. The proposed approach integrates a high-performance dynamic evolutionary algorithm and a reinforcement learning algorithm to generate safe and efficient drone routes and then augments the generated routes with dynamically computed drone reflexes to prevent collisions with unforeseen obstacles in the flying zone. We also present a parallel implementation of the proposed approach and evaluate it against two benchmarks. The results show that the proposed approach maximizes safety and generates highly efficient drone routes.

Background: Application Programming Interfaces (APIs) are key to software reuse. Software developers can link functionality and behaviour found in other software with their own software by taking an API into use. However, figuring out how an API works is usually demanding, and may require that the developers spend a notable amount of time familiarizing themselves with the API. Good API documentation is of key importance to simplify this task. Objective: To present a comprehensive, unbiased overview of the state-of-the-art on tools and approaches for API documentation generation. Method: A systematic mapping study on published tools and approaches that can be used for generating API documentation, or for assisting in the API documentation process. Results: 36 studies on API documentation generation tools and approaches analyzed and categorized in a variety of ways. Among other things, the paper presents an overview of what kind of tools have been developed, what kind of documentation they generate, and what sources the documentation approaches require. Conclusion: Out of the identified approaches, many contribute to API documentation in the areas of natural language documentation and code examples and templates. Many of the approaches contribute to ease API users' understanding and learning of the API, but also to the maintenance and generation of API documentation. Most of the approaches are automatic, simplifying the API documentation generation notably, under the assumption that relevant sources for the generation are available. Most of the API documentation approaches are evaluated either by exercise of the approach followed by analysis of the results, or by empirical evaluation methods.

Swarms of drones are increasingly been used in a variety of monitoring and surveillance, search and rescue, and photography and filming tasks. However, despite the growing popularity of swarm-based applications of drones, there is still a lack of approaches to generate efficient drone routes while minimizing the risks of drone collisions. In this paper, we present a novel approach that integrates learning, optimization, and prediction for generating efficient and safe routes for swarms of drones. The proposed approach comprises three main components: (1) a high-performance dynamic evolutionary algorithm for optimizing drone routes, (2) a reinforcement learning algorithm for incorporating the feedback and runtime data about the system state, and (3) a prediction approach to predict the movement of drones and moving obstacles in the flying zone. We also present a parallel implementation of the proposed approach and evaluate it against two benchmarks. The results demonstrate that the proposed approach allows to significantly reduce the route lengths and computation overhead while producing efficient and safe routes.

We present a model and system for deciding on computing versus storage trade-offs in the Cloud using von Neumann-Morgenstern lotteries. We use the decision model in a video-on-demand system providing cost-efficient transcoding and storage of videos. Video transcoding is an expensive computational process that converts a video from one format to another. Video data are large enough to cause concern over rising storage costs. In the general case, our work is of interest when dealing with expensive computations that generate large results that can be cached for future use. Solving the decision problem entails solving two sub-problems: how long to store cached objects and how many requests we can expect for a particular object in that duration. We compare the proposed approach to always storing and to our previous approach over one year using discrete-event simulations. We observe a 72% cost reduction compared to always storing and a 13% reduction compared to our previous approach. This reduction in cost stems from the proposed approach storing fewer unpopular objects when it does not regard it as cost-efficient to do so.

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mode. On the other hand, achieving the desired level of Quality of Service (QoS) between cloud providers and their users is critical. Therefore, the main challenge is to reduce energy consumption of data centers while satisfying QoS requirements. In this paper, we present a distributed system architecture to perform dynamic VM consolidation to reduce energy consumption of cloud data centers while maintaining the desired QoS. Since the VM consolidation problem is strictly NP-hard, we use an online optimization metaheuristic algorithm called Ant Colony System (ACS). The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function. Experimental results on real workload traces show that ACS-VMC reduces energy consumption while maintaining the required performance levels in a cloud data center. It outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.

As the scale of a cloud data center becomes larger and larger, the energy consumption of the data center also grows rapidly. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy by turning off unused Physical Machines (PMs) in data centers. In this paper, we present a distributed controller to perform dynamic VM consolidation to improve the resource utilizations of PMs and to reduce their energy consumption. Moreover, we use the ant colony system to find a near-optimal VM placement solution based on the specified objective function. Experimental results on the real workload traces from more than a thousand PlanetLab VMs show that the proposed approach reduces energy consumption and maintains required performance levels in a large-scale data center.

Infrastructure as a Service (IaaS) clouds provide virtual machines (VMs) under a pay-per-use business model, which can be used to create a dynamically scalable cluster of servers to deploy one or more web applications. In contrast to the traditional dedicated hosting of web applications where each VM is used exclusively for one particular web application, the shared hosting of web applications allows improved VM utilization by sharing VM resources among multiple concurrent web applications. However, in a shared hosting environment, dynamic scaling alone does not minimize over-provisioning of VMs. In this paper, we present a novel approach to consolidate multiple web applications in a cloud-based shared hosting environment. The proposed approach uses Ant Colony Optimization (ACO) to build a web application migration plan, which is then used to minimize over-provisioning of VMs by consolidating web applications on under-utilized VMs. The proposed approach is demonstrated in discrete-event simulations and is evaluated in a series of experiments involving synthetic as well as realistic load patterns.

Infrastructure as a Service (IaaS) clouds provide virtual machines (VMs) under the pay-per-use business model. The dynamic on-demand provisioning of VMs allows IaaS users to ensure scalability of their web applications and web-based services from really low to really high loads. However, VM provisioning must be done carefully because over-provisioning results in an increased operational cost, while under-provisioning leads to a sub par service. In this research work, our main focus is on cost-efficient VM provisioning for multi-tier web applications and video transcoding. Moreover, to prevent provisioned VMs from becoming overloaded, we augment VM provisioning with an admission control mechanism. Similarly, to ensure efficient use of provisioned VMs, under-utilized VMs are consolidated periodically. Since cost-efficient VM provisioning is an optimization problem, we apply metaheuristic approaches to find a near-optimal solution.

This paper presents a novel approach for stream-based admission control and job scheduling for video transcoding called SBACS (Stream-Based Admission Control and Scheduling). SBACS uses queue waiting time of transcoding servers to make admission control decisions for incoming video streams. It implements stream-based admission control with per stream admission. To ensure efficient utilization of the transcoding servers, video streams are segmented at the Group of Pictures level. In addition to the traditional rejection policy, SBACS also provides a stream deferment policy, which exploits cloud elasticity to allow temporary deferment of the incoming video streams. In other words, the admission controller can decide to admit, defer, or reject an incoming stream and hence reduce rejection rate. In order to prevent transcoding jitters in the admitted streams, we introduce a job scheduling mechanism, which drops a small proportion of video frames from a video segment to ensure continued delivery of video contents to the user. The approach is demonstrated in a discrete-event simulation with a series of experiments involving different load patterns and stream arrival rates.

This paper presents prediction-based dynamic resource allocation algorithms to scale video transcoding service on a given Infrastructure as a Service cloud. The proposed algorithms provide mechanisms for allocation and deallocation of virtual machines (VMs) to a cluster of video transcoding servers in a horizontal fashion. We use a two-step load prediction method, which allows proactive resource allocation with high prediction accuracy under real-time constraints. For cost-efficiency, our work supports transcoding of multiple on-demand video streams concurrently on a single VM, resulting in a reduced number of required VMs. We use video segmentation at group of pictures level, which splits video streams into smaller segments that can be transcoded independently of one another. The approach is demonstrated in a discrete-event simulation and an experimental evaluation involving two different load patterns.

This paper presents a prediction-based dynamic resource allocation approach for web applications called CRAMP (Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling). The proposed approach provides automatic deployment and proactive scaling of multiple simultaneous web applications on a given Infrastructure as a Service cloud in a shared hosting environment. It monitors and uses resource utilization metrics and does not require a performance model of the applications or the infrastructure dynamics. The shared hosting environment allows us to share virtual machine (VM) resources among deployed applications, reducing the number of required VMs. The approach is demonstrated in a prototype implementation that has been deployed in the Amazon Elastic Compute Cloud.

In this paper, we propose a test case generation technique based on Object-Z specification of a class, based on formalization of the test case generation strategy. The proposed technique enables automatic generation of test cases from an Object-Z specification, under the specified strategy. We also propose to enhance the prototype tool TinMan, which was originally developed to automate derivation and management of testing information for specificationbased class testing, by introducing semantics knowledge for the application of test case generation strategies in a format acceptable to the tool.