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Runtime Verification in both traditional systems and smart contracts has typically been implemented at the application level. Such systems very often run on Virtual Machines which execute their application logic. Conditional monitoring is often implemented to enable for certain monitors to be switched off in aim of reducing execution overheads once certain levels of assurance have been provided. Even when turned off such application level conditional monitoring still incurs added overheads to execute the conditional statement related operations. In this paper, we propose methods to support conditional runtime verification of applications at the virtual machine level. We demonstrate that such an approach can provide lower overheads in terms of both execution and gas.

The success of software applications, in a worldwide setup offering simple development and distribution models, is often determined by the quality and ease of use of provided interfaces. In this paper, we present a framework for multimodal signal analysis operating in conjunction with any other Android application to estimate the cognitive load imposed by its interface. The framework integrates seamlessly with such applications, even existing ones, by acting as middleware between the sensors layer and the application logic. The interaction between interface elements is mainly targeted in this work: a game presenting an increasingly complex interface was designed and tracking modules for touch events and eye movements were implemented. We show that the framework is able to capture and present raw data together with underlying models estimated by least squares approximation. We then discuss the implications of such a framework for the evaluation of efficient application interfaces.

Today’s distributed systems have to work in changing environments and under different working conditions. To provide high performance under these changing conditions, many distributed systems implement adaptive behavior. While simple adaptation through parameter tuning can only react to a limited range of conditions, a switch between different mechanisms at runtime enables broader adaptivity. However, distributed systems that switch mechanisms at runtime lack a clear abstraction for the adaptive behavior and, thus, usually interleave the adaptation and actual application logic. This leads to complex and error-prone systems that are hard to maintain and not easy to extend.

In this paper, we propose an approach for the model-driven development of portable applications that use native device features. A model is based on a finite-state machine which specifies GUIs, transitions and data-flow among application screens. The source code is generated starting from that model. The state application logic is described by JavaScript. The approach has been developed within an integrated development environment.

Requirements play a pivotal role in software development because they express the needs of the customer. A quality software system can emerge only when the real needs of the client are discovered. However, this is not enough. A typical software development project faces the problem of translating the user needs into a working system. These problems are dealt with by hundreds of books on various aspects of software design and pertaining to the plethora of software development technologies we can choose from. Related activities produce important artefacts that are treated as primary in software development: design models and code. Software design and coding directly contributes to the final system, and thus their results are treated as first-class citizens in the world of software development.

Genome data exists in many different formats in distributed data sources. Due to the many possible uses, various tools have been created for analysis and visualization. The huge size of the data sets combined with limited network capacity makes it very time-consuming to use cloud applications. In this contribution, I propose to transfer application logic to the data instead. This can be achieved by making the apps work independently from how and where the data is stored. In-memory databases enable this through on-the-fly data transformations and specialized query execution plans for distributed data.