In the realm of Autonomous Systems, the absence of direct human oversight introduces novel challenges as these systems start forming complex relationships. The emergent dynamics raise concerns regarding the fair distribution of resources and the promotion of altruistic behavior, which are traditionally moderated by human intervention. This paper envisions an innovative approach that integrates the monetization of acts of generosity in autonomous ecosystems to foster benevolent actions among autonomous agents and, therefore, promote the fairness of the ecosystem as a whole.

The rapid adoption of technology led to the rapid growth of various fields, including Unnamed Aerial Vehicles (UAV).Digital Twin (DT) became a popular concept to facilitate this progress, serving as a virtual replica of the physical drones to support run-time compliance checking, coordination, and analysis in trustworthy UAV design and operation.Nevertheless, the DT technology in UAV often lacks a precise specification and clear explanation of its characteristics, parameters, and functionalities.To address this gap, this paper investigates current research in DT applications for autonomous drones and compiles the findings towards the design of a DT to support the UAV sector.To this end, we extract the DT characteristics from existing papers and leverage these insights to propose a DT design for autonomous drones.The resulting DT is foundational in facilitating seamless collaboration and decision-making among collaborating autonomous drones in autonomous ecosystems to ensure safe and trustworthy operation, as demonstrated in a proof of concept, demonstrated through a case study of logistics shipment, showcasing the DT application for autonomous drones' collaboration in autonomous ecosystems.

As autonomous technologies blend into a variety of industries, the employment of Multi-Robot Systems (MRS) in complex tasks is becoming increasingly prevalent.These systems, characterized by their distributed intelligence and collaborative capabilities, are now largely deployed in executing critical missions ranging from environmental exploration to disaster response.To support the trustworthy execution of such collaborative multi-robot missions, the existence of an underlying trust management framework is becoming imperative, given the rising risks of malicious intruders attempting to join the MRS.This paper proposes a Trust Management Framework (TMF) for Multi-Robot Systems, to bridge the gap of scarce trust management support for MRS.By analyzing trust dynamics and integrating direct and indirect trust with contextual data, we address the trust vulnerabilities inherent to MRS.Our primary contribution is the development of a novel TMF significantly supporting the trust, security and reliability of MRS.The paper systematically outlines the evolution of our TMF, from theoretical underpinnings to an integrated solution, and the discussion of its impact on the trustworthiness of cooperative robotic networks.

Blockchain, as a trustless network, has provided diverse benefits for a wide range of application domains, such as enhancing data management in terms of data security, traceability, accountability, transparency, and decentralization. However, the detection of cybersecurity vulnerabilities in blockchain has initiated a debate on whether this inherently trustless technology needs further trust support or not. In this work, we explore mutual trust and trustless cooperation. First, we examine blockchain-assisted trust management to highlight the specific trustless trait of blockchain. Then, as consensus is an important component of blockchain technology, we examine the role of trust in evaluating the trustworthiness of peer participants in the blockchain consensus process and enhancing the growth of a consistent chain. Finally, we derive research findings in the promising cooperation between trustless and trust.

Abstract Software engineers widely acknowledge the inclusion of security requirements in the early stages of the development process. However, the need to prepare the software for the failure of the implemented security controls and subsequent investigation of the incident is often not discussed. Forensic‐ready software systems represent an evolution of secure systems being designed for the eventual digital forensic investigation. However, their exact properties remain largely unexplored, beyond preliminary high‐level conceptualizations of requirements and capabilities. Further obstacles hindering the adoption of forensic‐ready software systems are the different priorities and goals of involved parties and a gap in the digital forensics expertise of software engineers. In this paper, we conduct an empirical qualitative study identifying the problems and needs of forensic readiness while framing the notion of an ideal forensic‐ready software system and how it should treat potential evidence. To this end, we conducted semisupervised interviews with digital forensics experts on their idea, experience, and suggestions. The results provide insights into the needs of the experts to facilitate the definition of correct requirements towards forensic‐ready software systems to support the anticipated investigations properly.

Abstract While approaches aimed at developing forensic-ready systems are starting to emerge, it is still primarily a theoretical concept. This paper presents a case study of integrating forensic readiness capabilities into SensitiveCloud, an information system for storing and processing sensitive data. A risk-based approach to forensic readiness design is followed to achieve it. Consequently, weaknesses in both processes and systems are identified, and forensic readiness requirements are formulated. This case study reports on lessons learned in a practical implementation of a forensic-ready system, its impact on security, and its support towards ISO/IEC 27k.

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Abstract Forensic-ready software systems enhance the security posture by designing the systems prepared for potential investigation of incidents. Yet, the principal obstacle is defining their exact requirements, i.e., what they should implement. Such a requirement needs to be on-point and verifiable. However, what exactly comprises a forensic readiness requirement is not fully understood due to distinct fields of expertise in software engineering and digital forensics. This paper describes a forensic readiness qualitative factor reference model that enables the formulation of specific requirements for forensic-ready software systems. It organises the qualitative properties of forensic readiness into a taxonomy, which can then be used to formulate a verifiable requirement targeted at a specific quality. The model is then utilised in an automated valet parking service to define requirements addressing found inadequacies regarding a potential incident investigation.

With the increasing pervasiveness of software solutions, which are joining cyber-physical spaces and forming partnerships with humans, the importance of the trustworthiness of these systems is growing. At the same time, however, trustworthiness assurance is becoming extremely difficult in these complex ecosystems due to the high autonomy, unpredictability and limited controllability of their individual players. To mitigate safety risks for humans, these Dynamic Autonomous Ecosystems (e.g., Smart Cities) might require their member systems (e.g., Autonomous Vehicles) to execute software modules called Smart Agents to ensure safe coordination among themselves. Unfortunately, this technology is currently in its very early development with many challenges ahead. Namely, there is no guaranteed way of ensuring that these agents run on the right piece of hardware, with the right privileges required to fulfill their roles, and without the execution environment tampering with their instructions. This way, the host system (e.g., the Autonomous Vehicle we need to control for the sake of the safety of other ecosystem members) can escape the actual safety measures to be enforced. In this paper, we are proposing a novel software architecture that focuses on the detection of instruction tampering and privileged access in Smart Agents, and this way support the vision of trustworthy and safe evolution of Dynamic Autonomous Ecosystems.

Insider attacks in organizations are currently one of the most crucial cybersecurity challenges. Traitors are one of the most dangerous types of insider attackers. They are difficult to detect because they know the organization, processes, defense mechanisms, and employees. Besides abusing their rights and accesses, they can use their co-workers’ rights and accesses. They can hide their activities to perform attacks inconspicuously. The current masquerader detection techniques usually rely on the fact that the masquerader is an outsider, making them unable to detect attacks from the traitor. To detect these insider attackers, which we call masquerading traitors, we propose the human-as-solution approach and engage the employees in deciding whether the usage of their rights and accesses is suspicious and, therefore, might be performed by a masquerading traitor.

Cyberattacks perpetrated by insiders are difficult to prevent using traditional security approaches. Often, such attackers misuse legitimate access to the system to conduct an attack, or an external attacker manipulates or masquerades as an insider to gain access, bypassing the security controls. A possible solution to this problem are forensic-ready software systems that support the eventual forensic investigation. For example, assuring that appropriate evidence of an attack would be generated and assessable if needed. While not primarily aimed at prevention, the controls of forensic-ready systems can be used to ensure reliable post-incident investigation in the case of an insider attack. Currently, however, there is a gap in adequate methods for identifying requirements and assessment of such systems. Therefore, we propose FR-ISSRM, a risk management approach to derive the forensic readiness requirements addressing insider attacks. The requirements, once implemented, assist in the reliable uncovering culprit, root cause, damage of the attack, and overall improvement of security posture. The approach is then demonstrated in three cases covering typical insider attacks.

Manufacturing production heavily depends on the processes that need to be followed during manufacturing. As there might be many reasons behind possible deviations from these processes, the deviations can also cover ongoing insider attacks, e.g., intended to perform sabotage or espionage on these infrastructures. Insider attacks can cause tremendous damage to a manufacturing company because an insider knows how to act inconspicuously, making insider attacks very hard to detect. In this paper, we examine the potential of process-mining methods for insider-attack detection in the context of manufacturing, which is a new and promising application context for process-aware methods. To this end, we present five manufacturing-related scenarios of insider threats identified in cooperation with a manufacturing company, where the process mining could be most helpful in the detection of their respective attack events. We describe these scenarios and demonstrate the utilization of process mining in this context, creating ground for further future research.

With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes.

Software quality sits at the core of software engineering as a discipline. Yet, although each university software-engineering and the software-development course covers software quality to some extent, practitioners still lament on graduates’ readiness for practise for this very reason—poor quality of their code. As a result, we have engaged university industrial partners in designing a master-degree Software Quality course that puts the key software quality topics in one place.

The present time in the industry is a time where Android Applications are in a wide range with its widespread of the users also. With the increased use of Android applications, the defects in the Android context have also been increasing. The malware of defective software can be any pernicious program with malignant effects. Many techniques based on static, dynamic, and hybrid approaches have been proposed with the combination of Machine learning (ML) or Artificial Intelligence (AI) techniques. In this regard. Scientifically, it is complicated to examine the malignant effects. A single approach cannot predict defects alone, so multiple approaches must be used simultaneously. However, the proposed techniques do not describe the types of defects they address. The paper aims to propose a framework that classifies the defects. The Artificial Intelligence (AI) techniques are described, and the different defects are mapped to them. The mapping of defects to AI techniques is based on the types of defects found in the Android Context. The accuracy of the techniques and the working criteria has been set as the mapping metrics. This will significantly improve the quality and testing of the product. However, the appropriate technique for a particular type of defect could be easily selected. This will reduce the cost and time efforts put into predicting defects.

Recently, microservices have been examined as a solution for reshaping and improving the flexibility, scalability, and maintainability of critical infrastructure systems. However, microservice systems are also suffering from the presence of a substantial number of potentially vulnerable components that may threaten the protection of critical infrastructures. To address the problem, this paper proposes to leverage the concept of antifragility built in a framework for building self-learning microservice systems that could be strengthened by faults and threats instead of being deteriorated by them. To illustrate the approach, we instantiate the proposed approach of autonomous machine learning through an experimental evaluation on a benchmarking dataset of microservice faults.

The hands-on cybersecurity training quality is crucial to mitigate cyber threats and attacks effectively. However, practical cybersecurity training is strongly process-oriented, making the post-training analysis very difficult. This paper presents process-mining methods applied to the learning analytics workflow. We introduce a unified approach to reconstructing behavioral graphs from sparse event logs of cyber ranges. Furthermore, we discuss significant data features that affect their practical usability for educational process mining. Based on that, methods of dealing with the complexity of process graphs are presented, taking advantage of the puzzle-based gamification of in-class training sessions.

The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.

The 21st century is the age of automation. The automotive industry is converging towards deployment of complete automation by 2030. But are humans ready for it, or will they be hesitant to adopt it due to the lack of trust? To safeguard future autonomous mobility, robust run-time trust assurance and assessment is necessary. One strategy that is so far under-explored is rooted in involving the intelligence inside the autonomous agents, which could be directed towards detection of trust-breaking behaviour in other agents so that problematic vehicles are reported before they can engage in harmful behaviour.To support the progress in this direction, we propose a peer-to-peer model-based run-time trust assessment method, employing the model in terms of a Digital Twin for an autonomous vehicle (drone in our case) to ensure the trusted execution of intelligent agents. In this research, we examine the role of the Digital Twin in the trust-building scenario, and propose the characteristics of the intended Digital Twin model. To illustrate the approach, we present a case study of an autonomous-drone food delivery system and use formal approaches such as Petri Nets and Finite State Machines (FSM) to evaluate the scenario and demonstrate how trust could be built among autonomous drones or other vehicles.

Trust is a fundamental aspect in enabling self-adaptation of intelligent systems and in paving the way towards a smooth adoption of technological innovations in our societies. While Artificial Intelligence (AI) is capable to uplift the human contribution to our societies while protecting environmental resources, its ethical and technical trust dimensions bring significant challenges for a sustainable self-adaptive evolution in the domain of safety-critical systems. Inspired from the safety assurance case, in this paper we introduce the concept of trust assurance case together with the implementation of its ethical and technical principles directed towards assuring a trustworthy sustainable evolution of safety-critical AI-controlled systems.

Abstract We live at an exciting time where software has become a dominant aspect of our everyday life. Although software provides opportunities for improving various aspects of our society, it also presents many challenges. One of them is development, deployment, and sustaining of high quality software on a broad scale. While agile methods (Scrum being one of the most prominent examples) ease the process, their popularity deteriorates the clarity and simplicity they were once meant to bring into software development. This article explores the synergy of Scrum and Essence, a domain model of software engineering processes, intending to become a common ground for software development methods, bringing clarity into the composition of methods from individual practices. This short communication motivates the interplay of Scrum and Essence, being accompanied with a set of videotutorials and 21 Scrum Essential cards to further guide more effective team's way of working.

The world is facing an energy crisis. The smart building concept is presented by many researchers using IoT devices that do not have sufficient computational power to compute the data to decide about the results. According to an estimation, there will be 50 billion IoT devices in 2020. Most IoT devices send data to the cloud for processing. Latency and network usage will be increased in cloud servers because the cloud servers will not be able to handle millions of requests spontaneously. In this paper, we have proposed a framework that minimizes latency and energy consumption in cloud computing. The proposed framework uses edge computing and most of the processing is performed on fog nodes. The framework contributes significantly to energy saving by supporting behavioral and physical changes in the cloud network.

In our digital era, insider attacks are among the serious underresearched areas of the cybersecurity landscape.A significant type of insider attack is facilitated by employees without malicious intent.They are called unintentional perpetrators.We proposed mitigating these threats using a simulationgame platform to detect the potential attack vectors.This paper introduces and implements a scenario that demonstrates the usability of this approach in a case study.This work also helps to understand players' behavior when they are not told upfront that they will be a target of social engineering attacks.Furthermore, we provide relevant acquired observations for future research.

With the increasing threat of cybercrime, there is also an increasing need for the forensic investigation of those crimes. However, the topic of systematic preparation on the possible forensic investigation during the software development, called forensic readiness, has only been explored since recently. Thus, there are still many challenges and open issues. One of the obstacles is ensuring the correct implementation. Moreover, the growing volume and variety of digital evidence produced by the systems have to be put into consideration. It is especially important in the critical information infrastructure domain where potential cyberattacks could impact the safety of people. In this paper, we present research towards verification of forensic readiness in software development, with a focus on digital evidence they produce, to assist the advancement of this research domain. Furthermore, we formulate a process that serves a template for designing, developing, and refining a verification method for forensic-ready software systems.

Digitalization is currently the key factor for progress, with a rising need for storing, collecting, and processing large amounts of data. In this context, NoSQL databases have become a popular storage solution, each specialized on a specific type of data. Next to that, the multi-model approach is designed to combine benefits from different types of databases, supporting several models for data. Despite its versatility, a multi-model database might not always be the best option, due to the risk of worse performance comparing to the single-model variants. It is hence crucial for software engineers to have access to benchmarks comparing the performance of multi-model and single-model variants. Moreover, in the current Big Data era, it is important to have cluster infrastructure considered within the benchmarks. In this paper, we aim to examine how the multi-model approach performs compared to its single-model variants. To this end, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We do so in the cluster setup, to enhance state of the art in database benchmarks, which is not yet giving much insight into cluster-operating database performance.

Big Data processing has become crucial in many domains because the amount of the produced data has enormously increased almost everywhere. The effective selection of the right Big Data processing tool is hard due to the high number and large variety of the available state-of-the-art tools. Many research results agree that there is no one best Big Data solution for all needs and requirements. It is therefore essential to be able to navigate more efficiently in the world of Big Data processing tools. In this paper, we present a map of current Big Data processing tools, recommended according to their capabilities and advantageous properties identified in previously published academic benchmarks. This map—as a navigation diagram—is aimed at helping researchers and practitioners to filter a large amount of available Big Data processing tools according to the requirements and properties of their tasks. Additionally, we provide recommendations for future experiments comparing Big Data processing tools, to improve the navigation diagram.

Nowadays, a variety of Big Data architectures are emerging to organize the Big Data life cycle. While some of these architectures are proposed for general usage, many of them are proposed in a specific application domain such as smart cities, transportation, healthcare, and agriculture. There is, however, a lack of understanding of how and why Big Data architectures vary in different domains and how the Big Data architecture strategy in one domain may possibly advance other domains. Therefore, this paper surveys and compares the Big Data architectures in different application domains. It also chooses a representative architecture of each researched application domain to indicate which Big Data architecture from a given domain the researchers and practitioners may possibly start from. Next, a pairwise cross-domain comparison among the Big Data architectures is presented to outline the similarities and differences between the domain-specific architectures. Finally, the paper provides a set of practical guidelines for Big Data researchers and practitioners to build and improve Big Data architectures based on the knowledge gathered in this study.

With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an e > 0, the method computes an e-coreset of the dataset. The e-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

With the increasing adoption of microservice architecture and popularity of Platform as a Service (PaaS) cloud, software architecture design is in many domains leaning towards composition of loosely interconnected services hosted in the PaaS cloud, which in comparison to traditional multitier applications introduces new design challenges that software architects need to face when aiming at high scalability and resilience. In this paper, we study the key design decisions made during microservice architecture design and deployment in PaaS cloud. We identify major challenges of microservice architecture design in the context of the PaaS cloud, and examine the effects of architectural tactics and design patterns in addressing them. We apply selected tactics on a sample e-commerce application, constituting of microservices operated by Azure Service Fabric and utilizing other supportive PaaS cloud services within Microsoft Azure. The impact of the examined design decisions on the throughput, response time and scalability of the analyzed application is evaluated and discussed.

3D data have been widely used to represent complex data objects in different domains such as virtual reality, 3D printing or biological data analytics. Due to complexity of 3D data, it is usually featured as big 3D data. One of the typical big 3D data is the protein data, which can be used to visualize the protein structure in a 3D style. However, the 3D data also bring various data quality problems, which may cause the delay, inaccurate analysis results, even fatal errors for the critical decision making. Therefore, this paper proposes a novel big 3D data process model with specific consideration of 3D data quality. In order to validate this model, we conduct a case study for cleaning and analyzing the protein data. Our case study includes a comprehensive taxonomy of data quality problems for the 3D protein data and demonstrates the utility of our proposed model. Furthermore, this work can guide the researchers and domain experts such as biologists to manage the quality of their 3D protein data.

Smart Home Networks (SHNs) are attracting increasing interest in the research, development, and professional communities, particularly with the fast adoption of Internet of Things (IoT) devices. However, because of the limited processing, storage, and communication capabilities of these devices as well as their proprietary technologies, SHNs are getting vulnerable to numerous security threats that can adversely affect their proper functioning. Granting access to the legitimate users/entities is, therefore, essential to prevent illegitimate users from unauthorized use of the available resources. We argue that current access control schemes are not suitable for IoT-based SHN. We, therefore, propose in this paper a novel Secure Multi-Level Authentication Scheme (SeMLAS) based on software mobile agents. In addition to enabling secure authentication between external peers and nodes at different hierarchical level of the SHN, our scheme can shorten the authentication process and detect malicious nodes via an appropriate use of mobile agents. The performance analysis of the implementation of SeMLAS with the NS2 tool is showing promising results. Indeed, compared with the OTP and TinyPK schemes, SeMLAS is achieving 48% and 24% less in terms of communication overhead and total energy consumption respectively.

Reliability is an essential attribute of mission-and safety-critical systems. Software Reliability Growth Models (SRGMs) are regression-based models that use historical failure data to predict the reliability-related parameters. At the moment, there is no dedicated tool available that would be able to cover the whole process of SRGMs data preparation and application from issue repositories, discouraging replications and reuse in other projects. In this paper, we introduce STRAIT, a free and open-source tool for automatic software reliability growth analysis which utilizes data from issue repositories. STRAIT features downloading, filtering and processing of data from provided issue repositories for use in multiple SRGMs, suggesting the best fitting SRGM with multiple data snapshots to consider software evolution. The tool is designed to be highly extensible, in terms of additional issue repositories, SRGMs, and new data filtering and processing options. Quality engineers can use STRAIT for the evaluation of their software systems. The research community can use STRAIT for empirical studies which involve evaluation of new SRGMs or comparison of multiple SRGMs.

The increasing size of software systems and the emergence of increasingly autonomous systems demands innovative software engineering practices. The way software is being developed and maintained is rapidly changing and must take into account multifaceted constraints like fast-changing and unpredictable markets, complex and changing customer requirements, pressures of shorter time-to-market, and rapidly advancing information technologies, to name just a few of these new aspects. To cope with such constraints, software is increasingly produced according to rapid continuous software engineering development processes. The International Conference on Software Architecture (ICSA), is the premier platform for academia and industry to join efforts in addressing these challenges, bringing innovative solutions to the problems we face in the software architecture domain.

With the rapid development of the Big Data and Internet of Things (IoT), Big Data technologies have emerged as a key data analytics tool in IoT, in which, data clustering algorithms are considered as an essential component for data analysis. However, there has been limited research that addresses the challenges across Big Data and IoT and thus proposing a research agenda is important to clarify the research challenges for clustering Big Data in the context of IoT. By tackling this specific aspect - clustering algorithm in Big Data, this paper examines on Big Data technologies, related data clustering algorithms and possible usages in IoT. Based on our review, this paper identifies a set of research challenges that can be used as a research agenda for the Big Data clustering research. This research agenda aims at identifying and bridging the research gaps between Big Data clustering algorithms and IoT.

With the increasing popularity of Platform as a Service (PaaS) cloud, software architects need to face new challenges of software architecture design, efficiently integrating the benefits of PaaS services. In this respect, early quality evaluation of the designed PaaS cloud application is crucial to mitigate the risk of later architectural changes due to violation of quality requirements (such as throughput, response time and scalability). Despite the emergence of architectural tactics and patterns usable for the design of PaaS cloud applications, it is challenging for the software architects to navigate in a rich set of offered services, variability of the PaaS cloud environment and quality conflicts in existing tactics, which makes it almost impossible to predicts the overall application quality without time-consuming implementation of application prototypes. In this paper, we take advantage of the cloud elasticity and introduce a design-time quality evaluation technique for PaaS applications based on automatically generated application prototypes, which are deployed to the cloud and repeatedly evaluated in the context of multiple quality attributes and environment configurations. As part of this work, we present an abstract application model designed to carefully select the details with high performance impact, and present the implementation of our tool generating the prototypes of PaaS cloud applications.

One of the main goals of the power distribution utilities is to provide stable supply of the power load. The growing popularity of smart grids, i.e. power grids enhanced with modern ICT, opened new possibilities to make the grid more efficient, secure and reliable. However, by introducing new elements into the infrastructure, such as small-scale photovoltaic power plants, the management of power load is becoming more challenging.

In recent years, we have been witnessing profound transformation of energy distribution systems fueled by Information and Communication Technologies (ICT), towards the so called Smart Grid. However, while the Smart Grid design strategies have been studied by academia, only anecdotal guidance is provided to the industry with respect to increasing the level of grid intelligence. In this paper, we report on a successful project in assisting the industry in this way, via conducting a large anomaly-detection study on the data of one of the power distribution companies in the Czech Republic. In the study, we move away from the concept of single events identified as anomaly to the concept of collective anomaly, that is itemsets of events that may be anomalous based on their patterns of appearance. This can assist the operators of the distribution system in the transformation of their grid to a smarter grid. By analyzing Smart Meters data streams, we used frequent itemset mining and categorical clustering with clustering silhouette thresholding to detect anomalous behaviour. As the main result, we provided to stakeholders both a visual representation of the candidate anomalies and the identification of the top-10 anomalies for a subset of Smart Meters.

Platform as a Service (PaaS) is one of cloud service models associated with effective system operation in the presence of a rich set of platform services. Due to the complexity of the PaaS service model, software architects need to face various challenges when designing cloud applications that have to meet certain quality guarantees. In this paper we report on our experience with the design and implementation of highly scalable PaaS cloud applications, which resulted into deep understanding of the weaknesses and benefits of this service model, and helped us to identify and evaluate a number of architectural tactics for PaaS cloud applications. Besides the identification and evaluation of the tactics, this paper studies the mutual relationships and the impact of the tactics on various aspects of application's quality. The findings are demonstrated on a real-world case study of a complex private social network system, based on the Microsoft Azure cloud.

Power distribution systems worldwide are entering one of the biggest transformations since their inception.The Smart Grid has become a strategic term, which is associated with promising technology enhancement on one hand, but also high investment risks on the other hand.To maximize the former and minimize the latter, power distribution companies are searching for methods and tools to simulate and compare different Smart Grid design alternatives and deployment scenarios.In this paper, we draw upon our involvement in the modelling and simulation of the Smart Grid in the Czech Republic, and elaborate possible directions of Smart Grid modelling and analysis research to reach the "quality by design" before the actual Smart Grid realization.The discussion then details a specific design challenge together with its context within the design of the Czech Smart Grid.

Due to significant industrial demands toward software systems with increasing complexity and challenging quality requirements, software architecture design has become an important development activity and the research domain is rapidly evolving. In the last decades, software architecture optimization methods, which aim to automate the search for an optimal architecture design with respect to a (set of) quality attribute(s), have proliferated. However, the reported results are fragmented over different research communities, multiple system domains, and multiple quality attributes. To integrate the existing research results, we have performed a systematic literature review and analyzed the results of 188 research papers from the different research communities. Based on this survey, a taxonomy has been created which is used to classify the existing research. Furthermore, the systematic analysis of the research literature provided in this review aims to help the research community in consolidating the existing research efforts and deriving a research agenda for future developments.

With the increasing importance of reliability in business and industrial software systems, new techniques of architecture-based reliability engineering are becoming an integral part of the development process. These techniques can assist system architects in evaluating the reliability impact of their design decisions. Architecture-based reliability engineering is only effective if the involved reliability models reflect the interaction and usage of software components and their deployment to potentially unreliable hardware. However, existing approaches either neglect individual impact factors on reliability or hard-code them into formal models, which limits their applicability in component-based development processes. This paper introduces a reliability modeling and prediction technique that considers the relevant architectural factors of software systems by explicitly modeling the system usage profile and execution environment and automatically deriving component usage profiles. The technique offers a UML-like modeling notation whose models are automatically transformed into a formal analytical model. Our work builds upon the Palladio Component Model (PCM), employing novel techniques of information propagation and reliability assessment. We validate our technique with sensitivity analyses and simulation in two case studies. The case studies demonstrate effective support of usage profile analysis and architectural configuration ranking, together with the employment of reliability-improving architecture tactics.