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Abstract: Developers spend a substantial amount of their time with program comprehension. To improve their comprehension and refresh their memory, developers need to communicate with other developers, read the documentation, and analyze the source code. Many studies show that developers focus primarily on the source code and that small improvements can have a strong impact. As such, it is crucial to bring t...
Abstract: The concept of technical debt has been explored from many perspectives but its precise estimation is still under heavy empirical and experimental inquiry. We aim to understand whether, by harnessing approximate, data-driven, machine-learning approaches it is possible to improve the current techniques for technical debt estimation, as represented by a top industry quality analysis tool such as Sona...
Abstract: Context: Multiple types of processing units (e.g., CPUs, GPUs and FPGAs) can be used jointly to achieve better performance in computational systems. However, these units are built with fundamentally different characteristics and demand attention especially towards software deployment. Objective: The goal of this work is to summarize the state-of-the-art of software deployment on heterogeneous plat...
Abstract: Members: Premek Brada, University of West Bohemia, Czech Republic Gunnar Brataas, SINTEF, Norway Rajkumar Buyya, University of Melbourne, Australia Antonio Filieri, Imperial College London, UK Ian Gorton, Northeastern University Seattle, USA Wilhelm Hasselbring, Kiel University, Germany Nikolas Herbst, University of Würzburg, Germany Darko Huljenić, ENT Zagreb, Croatia Sebastian Lehrig, IBM Dublin...
Abstract: Online controlled experiments, known as A/B tests, have delivered tremendous value for businesses and customers. Traditionally, online controlled experiments running in search engines, websites, mobile apps, and other Business-to-Consumer (B2C) products use individual customers as randomization units. However, this may not be possible in Business-to-Business (B2B) products that are sold to, and us...
Abstract: Abstract Context: Adaptive systems are expected to cater for various operational contexts by having multiple strategies in achieving their objectives and the logic for matching strategies to an actual context. The prediction of relevant contexts at design time is paramount for dependability. With the current trend on using data mining to support the requirements engineering process, this task of u...
Abstract: Recently, an increasingly growing number of companies is focusing on achieving self-driving systems towards SAE level 3 and higher. Such systems will have much more complex capabilities than today's advanced driver assistance systems (ADAS) like adaptive cruise control and lane-keeping assistance. For complex software systems in the Web-application domain, the logical successor for Continuous Inte...
Abstract: Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the understanding of current and potential future use of ...
Abstract: Model-based development and continuous integration each separately are methods to improve the productivity of development of complex modern software systems. We investigate industrial adoption of these two phenomena in combination, i.e., applying continuous integration practices in model-based development projects. Through semi-structured interviews, eleven engineers at three companies with differ...
Abstract: Metrics, often visualized with dashboards, are considered crucial to help software start-ups focus on the right aspects during the first years. However, earlier research indicates, metric choices in emerging ecosystems are not necessarily the same as in literature, which mostly focuses on developed countries. More knowledge is required to provide dashboards that suite East African software startup...
Abstract: Deep learning is one of the most exciting and fast-growing techniques in Artificial Intelligence. The unique capacity of deep learning models to automatically learn patterns from the data differentiates it from other machine learning techniques. Deep learning is responsible for a significant number of recent breakthroughs in AI. However, deep learning models are highly dependent on the underlying ...
Abstract: Linux source-code automated maintenance (by analyzing 35 semantic patches applied to 19 versions of Linux) and (ii) fixing energy anti-patterns existing in Android applications available on GitHub (by analyzing 22 anti-patterns rewriting rules on 19 Android applications). In ‘‘Variability management in safety-critical systems design & dependability analysis,...
Abstract: Much has been investigated about software reuse since the software crisis. The development of software reuse methods, implementation techniques, and cost models has resulted in a significant amount of research over years. Nevertheless, the increasing adoption of reuse techniques, many of them subsumed under higher level software engineering processes, and advanced programming techniques that ease ...
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