The many configuration options of modern applications make it difficult for users to select a performance-optimal configuration. Performance models help users in understanding system performance and choosing a fast configuration. Existing performance modeling approaches for applications and configurable systems either require a full-factorial experiment design or a sampling design based on heuristics. This results in high costs for achieving accurate models. Furthermore, they require repeated execution of experiments to account for measurement noise. We propose Performance-Detective, a novel code analysis tool that deduces insights on the interactions of program parameters. We use the insights to derive the smallest necessary experiment design and avoiding repetitions of measurements when possible, significantly lowering the cost of performance modeling. We evaluate Performance-Detective using two case studies where we reduce the number of measurements from up to 3125 to only 25, decreasing cost to only 2.9% of the previously needed core hours, while maintaining accuracy of the resulting model with 91.5% compared to 93.8% using all 3125 measurements.

The security of software-intensive systems is frequently attacked. High fines or loss in reputation are potential consequences of not maintaining confidentiality, which is an important security objective. Detecting confidentiality issues in early software designs enables cost-efficient fixes. A Data Flow Diagram (DFD) is a modeling notation, which focuses on essential, functional aspects of such early software designs. Existing confidentiality analyses on DFDs support either information flow control or access control, which are the most common confidentiality mechanisms. Combining both mechanisms can be beneficial but existing DFD analyses do not support this. This lack of expressiveness requires designers to switch modeling languages to consider both mechanisms, which can lead to inconsistencies. In this article, we present an extended DFD syntax that supports modeling both, information flow and access control, in the same language. This improves expressiveness compared to related work and avoids inconsistencies. We define the semantics of extended DFDs by clauses in first-order logic. A logic program made of these clauses enables the automated detection of confidentiality violations by querying it. We evaluate the expressiveness of the syntax in a case study. We attempt to model nine information flow cases and six access control cases. We successfully modeled fourteen out of these fifteen cases, which indicates good expressiveness. We evaluate the reusability of models when switching confidentiality mechanisms by comparing the cases that share the same system design, which are three pairs of cases. We successfully show improved reusability compared to the state of the art. We evaluated the accuracy of confidentiality analyses by executing them for the fourteen cases that we could model. We experienced good accuracy.

During the development of large software-intensive systems, developers use several modeling languages and tools to describe a system from different viewpoints. Model-driven and view-based technologies have made it easier to define domain-specific languages and transformations. Nevertheless, using several languages leads to fragmentation of information, to redundancies in the system description, and eventually to inconsistencies. Inconsistencies have negative impacts on the system’s quality and are costly to fix. Often, there is no support for consistency management across multiple languages. Using a single language is no practicable solution either, as it is overly complex to define, use, and evolve such a language. View-based development is a suitable approach to deal with complex systems, and is widely used in other engineering disciplines. Still, we need to cope with the problems of fragmentation and consistency. In this paper, we present the Vitruvius approach for consistency in view-based modeling. We describe the approach by formalizing the notion of consistency, presenting languages for consistency preservation, and defining a model-driven development process. Furthermore, we show how existing models can be integrated. We have evaluated our approach at two case studies from component-based and embedded automotive software development, using our prototypical implementation based on the Eclipse Modeling Framework.

One of the most common usage scenarios for Cloud-IoT applications is Sensing-as-a-Service, which focuses on the processing of sensor data in order to make it available for other applications. Auto-scaling is a popular runtime management technique for cloud applications to cope with a varying resource demand by provisioning resources in an autonomous manner. However, if an auto-scaling system cannot provide the required resources, e.g., due to cost constraints, the cloud application is overloaded, which impacts its performance and availability. We present a feedback control mechanism to mitigate and recover from overload situations by adapting the send rate of smart devices in consideration of the current processing rate of the cloud application. This mechanism supports a coupling with the widely used threshold-based auto-scaling systems. In a case study, we demonstrate the capability of the approach to cope with overload scenarios in a realistic environment. Overall, we consider this approach as a novel tool for runtime managing cloud applications.

In recent years, the development of systems for processing and analyzing large amounts of data (so-called Big Data) has become an important sub-discipline of software engineering. However, to date there exits no comprehensive summary of the specific idiosyncrasies and challenges that the development of Big Data systems imposes on software engineers. With this paper, we aim to provide a first step towards filling this gap based on our collective experience from industry and academic projects as well as from consulting and initial literature reviews. The main contribution of our work is a concise summary of 26 challenges in engineering Big Data systems, collected and consolidated by means of a systematic identification process. The aim is to make practitioners more aware of common challenges and to offer researchers a solid baseline for identifying novel software engineering research directions.