Auflistung P267 - Software Engineering 2017 nach Erscheinungsdatum
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- KonferenzbeitragA Unified Model-Driven Approach for Extracting and Generating Workload Specifications for Load Testing and Performance Prediction of Application Systems(Software Engineering 2017, 2017) Hoorn, André van; Vögele, Christian; Hasselbring, Wilhelm; Krcmar, HelmutThis extended abstract summarizes our article on extracting probabilistic workload spec- ifications for load testing and performance prediction of session-based application systems, which has been published recently in the Journal on Software and Systems Modeling [Vo ̈16].
- KonferenzbeitragVergleich und Kombination von Techniken des Predictive Business Process Monitoring(Software Engineering 2017, 2017) Metzger, Andreas; Leitner, Philipp; Ivanovic, Dragan; Schmieders, Eric; Franklin, Rod; Carro, Manuel; Dustdar, Schahram; Pohl, KlausWir stellen einen experimentellen Vergleich von Prognosetechniken für das Predictive Business Process Monitoring vor. Ausgehen von unseren Experimentergebnissen schlagen wir eine geeignete Kombination von Prognosetechniken vor.
- KonferenzbeitragTesten und Qualitätssicherung – Einblick in die Finanz- und Versicherungsbranche(Software Engineering 2017, 2017) Vosseberg, Karin; Spillner, Andreas; Winter, MarioDie Hochschulen Bremerhaven, Bremen und die Technische Hochschule Köln haben zusammen mit dem German Testing Board (GTB) sowie dem Swiss Testing Board (STB) eine Neuauflage der 2011 erfolgreich durchgeführten Umfrage Softwaretest in der Praxis durchgeführt. Die Umfrage 2015/16 wurde um Forschungsfragen erweitert. Dabei haben über 650 Teilnehmende, gruppiert in Rollen aus dem oberen und mittleren Management, aus der operativen Umsetzung und aus der Forschung, jeweils auf diese Gruppen zugeschnittene Fragebögen beantwortet. Die befragten Manager, Tester und Entwickler sind zu 40% in dem Finanzdienstleistungs-/Versicherungsbereich tätig. Hier lohnt es sich einen speziellen Blick auf diese Branche zu werfen.
- KonferenzbeitragEmploying Classifying Terms for Testing Model Transformations(Software Engineering 2017, 2017) Gogolla, Martin; Vallecillo, Antonio; Burgueno, Loli; Hilken, Frank
- KonferenzbeitragModel-Driven Allocation Engineering – Abridged Version(Software Engineering 2017, 2017) Pohlmann, Uwe; Hüwe, Marcus
- KonferenzbeitragCase Studies for Bidirectional Transformations in QVT Relations(Software Engineering 2017, 2017) Westfechtel, BernhardQVT Relations (QVT-R), a standard issued by the Object Management Group (OMG), is a language for the declarative specification of model transformations. In particular, QVT-R supports the specification of bidirectional transformations: Rather than writing two unidirectional transfor- mations separately, a transformation developer may provide a single relational specification which may be executed in both directions (from source to target and vice versa). In this contribution, which is based on [We16], we summarize the main results from a series of case studies which shed a light on both potentials and limitations of QVT-R as a bidirectional transformation language.
- KonferenzbeitragForces that Support Agile Adoption in the Automotive Domain(Software Engineering 2017, 2017) Hohl, Philipp; Münch, Jürgen; Stupperich, Michael
- KonferenzbeitragIncreasing the Throughput of Pipe-and-Filter Architectures by Integrating the Task Farm Parallelization Pattern(Software Engineering 2017, 2017) Wulf, Christian; Hasselbring, WilhelmThe Pipe-and-Filter style represents a well-known family of component-based architectures. By executing each filter on a dedicated processing unit, it is also possible to leverage contemporary distributed systems and multi-core systems for a high throughput. However, this simple parallelization approach is not very effective when (1) the workload is uneven distributed over all filters and when (2) the number of available processing units exceeds the number of filters. In this paper, we explain how we utilize the task farm parallelization pattern in order to increase the throughput of Pipe-and-Filter architectures. Furthermore, we describe an associated modular self- adaptive mechanism which enables the automatic resource-efficient reaction on unevenly distributed workload. Finally, we refer to an extensive experimental evaluation of our self-adaptive task farm performed by us. The results show that our task farm (1) increases the overall throughput and (2) scales well according to the current workload.
- KonferenzbeitragAn Execution System for Self-healing Workflows in Cyber-physical Systems(Software Engineering 2017, 2017) Seiger, Ronny; Huber, Steffen; Schlegel, ThomasWithin the Internet of Things software controlled sensors, actuators and smart objects enable a close coupling of the cyber and physical worlds. Introducing workflows into these cyber-physical sys- tems (CPS) promises advantages regarding automation, resource utilization and flexibility of control systems. In this work, we present PROtEUS – an integrated system for process execution in CPS. PROtEUS integrates components for event processing, data routing, dynamic service invocation and human interaction. It is the basis for executing self-healing and model-based workflows that assure cyber-physical consistency by applying the MAPE-K feedback loop.
- KonferenzbeitragMonkey See, Monkey Do: Effective Generation of GUI Tests with Inferred Macro Events(Software Engineering 2017, 2017) Ermuth, Markus; Pradel, MichaelAutomated testing is an important part of validating the behavior of software with com- plex graphical user interfaces, such as web, mobile, and desktop applications. Despite recent ad- vances in UI-level test generation, existing approaches often fail to create complex sequences of events that represent realistic user interactions. As a result, these approaches cannot reach particular parts of the application under test, which then remain untested. This paper presents a UI-level test generation approach that exploits execution traces of human users to automatically create complex sequences of events that go beyond the recorded traces. The key idea is to infer so-called macro events, i.e., sequences of low-level UI events that correspond to a single logical step of interaction, such as choosing an item of a drop-down menu or filling and submitting a form. The approach builds upon and adapts well-known data mining techniques, in particular frequent subsequence mining and inference of finite state machines. We implement the approach for client-side web applications and apply it to four real-world applications. Our results show that macro-based test generation reaches more pages, exercises more usage scenarios, and covers more code within a fixed testing budget than a purely random test generator.