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P320 - Software Engineering 2022

https://dl.gi.de/handle/20.500.12116/37960
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Auflistung P320 - Software Engineering 2022 nach Autor:in "Bittner, Paul Maximilian"

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  • Konferenzbeitrag
    Feature Trace Recording - Summary
    (Software Engineering 2022, 2022) Bittner, Paul Maximilian; Schultheiß, Alexander; Thüm, Thomas; Kehrer, Timo; Young, Jeffrey M.; Linsbauer, Lukas
    In this work, we report about recent research on Feature Trace Recording, originally published at the Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2021. Tracing requirements to their implementation is crucial to all stakeholders of a software development process. When managing software variability, requirements are typically expressed in terms of features, a feature being a user-visible characteristic of the software. While feature traces are fully documented in software product lines, ad-hoc branching and forking, known as clone-and-own, is still the dominant way for developing multi-variant software systems in practice. Retroactive migration to product lines suffers from uncertainties and high effort because knowledge of feature traces must be recovered but is scattered across teams or even lost. We propose a semi-automated methodology for recording feature traces proactively, during software development when the necessary knowledge is present. To support the ongoing development of previously unmanaged clone-and-own projects, we explicitly deal with the absence of domain knowledge for both existing and new source code. We evaluate feature trace recording by replaying code edit patterns from the history of two real-world product lines. Our results show that feature trace recording reduces the manual effort to specify traces.
  • Konferenzbeitrag
    Scalable N-Way Model Matching Using Multi-Dimensional Search Trees - Summary
    (Software Engineering 2022, 2022) Schultheiß, Alexander; Bittner, Paul Maximilian; Thüm, Thomas; Kehrer, Timo
    In this work, we report about recent research on n-way model matching, originally published at the International Conference on Model Driven Engineering Languages and Systems (MODELS) 2021. Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN (Range Queries on N input models) in Java, and empirically evaluate the matching quality and runtime performance on several datasets of different origin and model type. Compared to the state-of-the-art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.