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M
Meng Meng, Jens Meinicke, Chu-Pan Wong, Eric Walkingshaw, Christian Kästner.  2017.  A Choice of Variational Stacks: Exploring Variational Data Structures. 11th International Workshop on Variability Modelling of Software-intensive Systems (VAMOS).

Many applications require not only representing variability in software and data, but also computing with it. To do so efficiently requires variational data structures that make the variability explicit in the underlying data and the operations used to manipulate it. Variational data structures have been developed ad hoc for many applications, but there is little general understanding of how to design them or what tradeoffs exist among them. In this paper, we strive for a more systematic exploration and analysis of a variational data structure. We want to know how different design decisions affect the performance and scalability of a variational data structure, and what properties of the underlying data and operation sequences need to be considered. Specifically, we study several alternative designs of a variational stack, a data structure that supports efficiently representing and computing with multiple variants of a plain stack, and that is a common building block in many algorithms. The different variational stacks are presented as a small product line organized by three design decisions. We analyze how these design decisions affect the performance of a variational stack with different usage profiles. Finally, we evaluate how these design decisions affect the performance of the variational stack in a real-world scenario: in the interpreter VarexJ when executing real software containing variability. 

J
Jens Meinicke, Chu-Pan Wong, Christian Kästner, Thomas Thum, Gunter Saake.  2016.  On essential configuration complexity: measuring interactions in highly-configurable systems. ASE 2016 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. :483-494.

Quality assurance for highly-configurable systems is challenging due to the exponentially growing configuration space. Interactions among multiple options can lead to surprising behaviors, bugs, and security vulnerabilities. Analyzing all configurations systematically might be possible though if most options do not interact or interactions follow specific patterns that can be exploited by analysis tools. To better understand interactions in practice, we analyze program traces to characterize and identify where interactions occur on control flow and data. To this end, we developed a dynamic analysis for Java based on variability-aware execution and monitor executions of multiple small to medium-sized programs. We find that the essential configuration complexity of these programs is indeed much lower than the combinatorial explosion of the configuration space indicates. However, we also discover that the interaction characteristics that allow scalable and complete analyses are more nuanced than what is exploited by existing state-of-the-art quality assurance strategies.