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To address the problems of traditional software development, recent

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Abstract. Automata-based interface and protocol specifications provide an elegant framework to capture and automatically verify the interactive behavior of component-based software systems. Unfortunately, the underlying formalisms suffer from combinatorial state explosion when constructing new specifications for composite components or systems and may therefore render the application of these techniques impractical for real-world applications. In this paper, we explore the bisimulation technique as a means for a mechanical state space reduction of component-based systems. In particular, we apply both strong and weak bisimulation to Component Interaction Automata in order to obtain a minimal automata that can serve as a behavioral equivalent abstraction for a given component specification and illustrate that the proposed approach can significantly reduce the complexity of an interface specification after composition.

Contemporary software systems are composed of many components, which, in general, undergo phased and incremental development. In order to facilitate the corresponding construction process, it is important that the development team in charge has a good understanding of how individual software components typically evolve. Furthermore, software engineers need to be able to recognize abnormal patterns of growth with respect to size, structure, and complexity of the components and the resulting composite. Only if a development team understands the processes that underpin the evolution of software systems, will they be able to make better development choices. In this paper, we analyze recurring structural and evolutionary patterns that we have observed in public-domain software systems built using object-oriented programming languages. Based on our analysis, we discuss common growth patterns found in present-day component-based software systems and illustrate simple means to aid developers in achieving a better understanding of those patterns. As a consequence, we hope to raise the awareness level in the community on how component-based software systems tend to naturally evolve.

We first provide an overview of the state-of-the-art architectures for continuous availability, briefly covering such traditional concepts as high-availability (HA) clustering on distributed platforms and on the mainframe. We explain how HA can be achieved in environments based on Sun Microsystems J2EEe, which differ from classical clustering approaches, and we discuss how disaster recovery (DR) has become an extension of HA. The paper then presents aspects of service management, including the use and orchestration of process-based (ITILt) systems management tasks within DR scenarios, where the key challenge is to ensure the right level of redundancy in the integration and service-oriented management of heterogeneous information technology landscapes.

Software service emulation is an emerging technique for creating realistic executable models of server-side behaviour. It is particularly useful in quality assurance and DevOps, replicating production-like conditions for large-scale enterprise software systems. Existing approaches can automatically build client-server and server-server interaction models of complex software systems directly from analysis of service interaction trace data. However, when these interaction traces become large, searching an entire trace library to generate a run-time responses can become very slow. In this paper we describe a new technique that utilises data mining, specifically clustering algorithms, to pre-process large amounts of recorded interaction trace data. With the obtained clusters we facilitate efficient yet well-formed runtime response generation in our Enterprise System emulation environment. We evaluate our approach using two common application-layer protocols: LDAP and SOAP. Our experimental results show that by utilising clustering techniques in the pre-processing step, the response generation time can be reduced by 99% on average compared with existing approaches.

Despite the significant increase in cybersecurity solutions investment, organizations are still plagued by security breaches, especially data breaches. As more organizations experience crippling security breaches, the wave of compromised data is growing significantly. The financial consequences of a data breach are set on the rise, but the cost goes beyond potential fines. Data breaches could have a catastrophic impact not only in loss of company's reputation and stock price, but also in economic terms. Threat Intelligence has been recently introduced to enable greater visibility of cyber threats, in order to better protect organizations' digital assets and prevent data breaches. Threat intelligence is the practice of integrating and analyzing disjointed cyber data to extract evidence-based insights regarding an organization's unique threat landscape. This helps explain who the adversary is, how and why they are comprising the organization's digital assets, what consequences could happen following the attack, what assets actually could be compromised, and how to detect or respond to the threat. Every organization is different and threat intelligence frameworks are custom-tailored to the business process itself and the organization's risks, as there is no "one-size-fits-all" in cyber. In this paper, we review the problem of data breaches and discuss the challenges of implementing threat intelligence that scales in today's complex threat landscape and digital infrastructure. This is followed by an illustration of how the future of effective threat intelligence is closely linked to efficiently applying Artificial Intelligence and Machine Learning approaches, and we conclude by outlining future research directions in this area.