http://dl.gi.de/handle/20.500.12116/34502 2026-07-21T12:49:41Z http://dl.gi.de/handle/20.500.12116/34557 Robotics Software Engineering: A Perspective from the Service Robotics Domain (Summary) García, Sergio; Strüber, Daniel; Brugali, Davide; Berger, Thorsten; Pelliccione, Patrizio Koziolek, Anne; Schaefer, Ina; Seidl, Christoph 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. 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/34556 Automated Reuse of Test Cases for Highly Configurable Software Systems Fischer, Stefan; Michelon, Gabriela Karoline; Ramler, Rudolf Ramler; Linsbauer, Lukas; Egyed, Alexander Koziolek, Anne; Schaefer, Ina; Seidl, Christoph In this work, we report about our research results initially published in the journal Empirical Software Engineering, volume 25, issue 6, pp. 5295–5332, November 2020, https://doi.org/10.1007/s10664-020-09884-x. We performed experiments on test reusability across configurations of highly configurable software systems. First, we used manually written tests for specific configurations of three configurable systems and investigated how changing configuration options affects these tests. Subsequently, we applied an approach developed for automated reuse, ECCO (Extraction and Composition for Clone-and-Own), to automatically generate tests for new configurations from the existing, manually written tests. The experiments showed that our generated tests had a higher or equal success rate compared to direct reuse and they generally achieved a higher code coverage. It can be concluded that automating the reuse of tests for configurable software can substantially reduce the effort for adapting existing tests and it supports a rigorous testing process. 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/34555 Specmate: Automated Creation of Test Cases from Acceptance Criteria Fischbach, Jannik; Vogelsang, Andreas; Spies, Dominik; Wehrle, Andreas; Junker, Maximilian; Freudenstein, Dietmar Koziolek, Anne; Schaefer, Ina; Seidl, Christoph We summarize the paper Specmate: Automated Creation of Test Cases from Acceptance Criteria, which was presented at the 2020 edition of the IEEE International Conference on Software Testing, Verification and Validation (ICST). 2021-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/34554 #ifdef Directives and Program Comprehension: The Dilemma between Correctness and Preference Fenske, Wolfram; Krüger, Jacob; Kanyshkova, Maria; Schulze, Sandro Koziolek, Anne; Schaefer, Ina; Seidl, Christoph In this extended abstract, we summarize our paper with the homonymous title published at the International Conference on Software Maintenance and Evolution (ICSME) 2020. 2021-01-01T00:00:00Z