http://dl.gi.de/handle/20.500.12116/37960 2026-07-21T13:38:50Z 2026-07-21T13:38:50Z Groner, Raffaela Juhnke, Katharina Höppner, Stefan Tichy, Matthias Becker, Steffen Vijayshree, Vijayshree Frank, Sebastian http://dl.gi.de/handle/20.500.12116/37997 2022-01-19T12:56:58Z 2022-01-01T00:00:00Z

A Survey on the Relevance of the Performance of Model Transformations Groner, Raffaela; Juhnke, Katharina; Höppner, Stefan; Tichy, Matthias; Becker, Steffen; Vijayshree, Vijayshree; Frank, Sebastian Grunske, Lars; Siegmund, Janet; Vogelsang, Andreas When we are confronted with performance issues in a general-purpose language, like Java, it is a given to us that we have various tools and techniques at our disposal to help us. But is such support also needed when using model transformation languages? To address this question, we conducted a quantitative online survey as part of a mixed methods study with 84 respondents to our questionnaire. Our results show that a certain performance is desired but not always achieved. The developers would like to improve the performance, but they lack insights on how a transformation is performed. As a first step to mitigate this issue, we compiled a list of information regarding the models used, the transformations applied and their execution deemed to be helpful by the participants. Additionally, we used hypotheses tests to investigate possible influencing factors that cause participants to try to improve the performance of transformations. The main relevant factors found in our study are the satisfaction with the execution time, the size of the models used, the relevance of whether a certain execution time is not exceeded in the average case, and the knowledge of how a transformation engine executes a transformation.

2022-01-01T00:00:00Z Gruber, Martin Lukasczyk, Stephan Kroiß, Florian Fraser, Gordon http://dl.gi.de/handle/20.500.12116/37998 2022-01-19T12:56:58Z 2022-01-01T00:00:00Z

An Empirical Study of Flaky Tests in Python Gruber, Martin; Lukasczyk, Stephan; Kroiß, Florian; Fraser, Gordon Grunske, Lars; Siegmund, Janet; Vogelsang, Andreas This is a summary of our work presented at the International Conference on Software Testing 2021 [Gr21b]. Tests that cause spurious failures without code changes, i.e., flaky tests, hamper regression testing and decrease trust in tests. While the prevalence and importance of flakiness is well established, prior research focused on Java projects, raising questions about generalizability. To provide a better understanding of flakiness, we empirically study the prevalence, causes, and degree of flakiness within 22 352 Python projects containing 876 186 tests. We found flakiness to be equally prevalent in Python as in Java. The reasons, however, are different: Order dependency is a dominant problem, causing 59% of the 7 571 flaky tests we found. Another 28% were caused by test infrastructure problems, a previously less considered cause of flakiness. The remaining 13% can mostly be attributed to the use of network and randomness APIs. Unveiling flaky tests also requires more runs than often assumed: A 95% confidence that a passing test is not flaky on average would require 170 reruns. Additionally, through our investigations, we created a large dataset of flaky tests that other researchers already started building on [MM21; Ni21].

2022-01-01T00:00:00Z Daun, Marian Grubb, Alicia M. Tenbergen, Bastian http://dl.gi.de/handle/20.500.12116/37993 2022-01-19T12:56:57Z 2022-01-01T00:00:00Z

Three Major Instructional Approaches for Requirements Engineering Daun, Marian; Grubb, Alicia M.; Tenbergen, Bastian Grunske, Lars; Siegmund, Janet; Vogelsang, Andreas In this talk, we report on our findings from the paper A Survey of Instructional Approaches in the Requirements Engineering Education Literature, which has been accepted at and published in the proceedings of the 2021 IEEE International Conference on Requirements Engineering. The paper reports the findings of a systematic literature review to define and investigate the current state of research on requirements engineering education.

2022-01-01T00:00:00Z Genfer, Patric Zdun, Uwe http://dl.gi.de/handle/20.500.12116/37995 2022-01-19T12:56:57Z 2022-01-01T00:00:00Z

Identifying Domain-Based Cyclic Dependencies in Microservice APIs Using Source Code Detectors Genfer, Patric; Zdun, Uwe Grunske, Lars; Siegmund, Janet; Vogelsang, Andreas Isolation, autonomy, and loose coupling are critical success factors of microservice architectures, but unfortunately, systems tend to become strongly coupled over time and sometimes even exhibiting cyclic communication chains. These cycles can even manifest on a conceptual or domain level, making them hard to track for algorithms that rely solely on static analysis. Accordingly, previous attempts to detect cycles either focused on synchronous communication or had to collect additional runtime data, which can be costly and time-consuming. We suggest a novel approach for identifying and evaluating domain-based cyclic dependencies in microservice systems based on modular, reusable source code detectors. Based on the architecture model reconstructed by our detectors, we derived a set of architectural metrics for identifying and classifying domain-based cyclical dependencies. By conducting two case studies on open-source microservice architectures, we validated the feasibility and applicability of our approach.

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