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P310 - Software Engineering 2021

https://dl.gi.de/handle/20.500.12116/34502
Auflistung nach:

Auflistung P310 - Software Engineering 2021 nach Autor:in "Berger, Thorsten"

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  • ConferencePaper
    An Empirical Analysis of the Costs of Clone- and Platform-Oriented Software Reuse
    (Software Engineering 2021, 2021) Krüger, Jacob; Berger, Thorsten
    In this extended abstract, we summarize our paper with the homonymous title published at the Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2020.
  • ConferencePaper
    Robotics Software Engineering: A Perspective from the Service Robotics Domain (Summary)
    (Software Engineering 2021, 2021) García, Sergio; Strüber, Daniel; Brugali, Davide; Berger, Thorsten; Pelliccione, Patrizio
    We present our paper published in the proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2020. Robots that support humans by performing useful tasks (a.k.a., service robots) are booming worldwide. In contrast to industrial robots, the development of service robots comes with severe software engineering challenges, since they require high levels of robustness and autonomy to operate in highly heterogeneous environments. As a domain with critical safety implications, service robotics faces a need for sound software development practices. In this paper, we present the first large-scale empirical study to assess the state of the art and practice of robotics software engineering. We conducted 18 semi-structured interviews with industrial practitioners working in 15 companies from 9 different countries and a survey with 156 respondents (from 26 countries) from the robotics domain. Our results provide a comprehensive picture of (i) the practices applied by robotics industrial and academic practitioners, including processes, paradigms, languages, tools, frameworks, and reuse practices, (ii) the distinguishing characteristics of robotics software engineering, and (iii) recurrent challenges usually faced, together with adopted solutions. The paper concludes by discussing observations, derived hypotheses, and proposed actions for researchers and practitioners.
  • ConferencePaper
    Variability Representations in Class Models: An Empirical Assessment (Summary)
    (Software Engineering 2021, 2021) Strüber, Daniel; Anjorin, Anthony; Berger, Thorsten
    We present our paper originally published in the proceedings of the ACM/IEEE International Conference on Model Driven Engineering Languages and Systems 2020 (MODELS). Owing to the ever-growing need for customization, software systems often exist in many different variants. To avoid the need to maintain many different copies of the same model, developers of modeling languages and tools have recently started to provide representations for such variant-rich systems, notably variability mechanisms that support the implementation of differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of them having unique benefits and drawbacks. Language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this paper, we present an empirical assessment of annotative and compositional variability mechanisms for class models. We report and discuss findings from an experiment with 73 participants, in which we studied the impact of the chosen variability mechanisms during model comprehension tasks. We find that, compared to the baseline of listing all model variants separately, the annotative technique did not affect developer performance. Use of the compositional mechanism correlated with impaired performance. For a subset of our tasks the annotative mechanism is preferred to the compositional one and the baseline. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of best established best practices from the code domain to models.