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Auflistung nach Autor:in "Hebig, Regina"

1 - 10 von 66
  • Konferenzbeitrag
    20th Workshop on Automotive Software Engineering (ASE’23)
    (Software Engineering 2023, 2023) Kugele, Stefan; Grunske, Lars
    Software-based systems play an increasingly important role and enable most innovations in modern cars. This workshop will address various topics related to automotive software development. The participants will discuss appropriate methods, techniques, and tools needed to address the most current challenges for researchers and practitioners.
  • Konferenzbeitrag
    5th Workshop on Avionics Systems and Software Engineering (AvioSE’23)
    (Software Engineering 2023, 2023) Annighoefer, Bjoern; Schweiger, Andreas; Poulaine, Stéphane
    Systems and software engineering in aerospace is subject to special challenges. For their resolution the AvioSE’23 workshop connects academia and industry with selected scientific presentations of high quality, motivating keynote talks, and an interactive panel discussion.
  • Konferenzbeitrag
    Anforderungsmanagement in Enterprise Systems-Projekten
    (Software Engineering 2023, 2023) Weiss, Christoph; Keckeis, Johannes
  • Konferenzbeitrag
    API Deprecation: A Systematic Mapping Study
    (Software Engineering 2023, 2023) Bonorden, Leif; Riebisch, Matthias
    This extended abstract is based on a study published at the 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2022). [BR22a] We conducted a systematic mapping study on API deprecation including 36 primary studies. Our analysis highlights five major gaps in research: studying remote APIs, investigating a broader range of static APIs, joining views of suppliers and clients, including humans in studies, and designing with deprecation in mind.
  • Konferenzbeitrag
    Automatisierte Identifikation von sicherheitsrelevanten Konfigurationseinstellungen mittels NLP
    (Software Engineering 2023, 2023) Stöckle, Patrick; Wasserer, Theresa; Grobauer, Bernd; Pretschner, Alexander
    Dieser Vortrag wurde auf der 37. IEEE/ACM International Conference on Automated Software Engineering (ASE) präsentiert [St22]. Um Computerinfrastrukturen zu sichern, müssen die verantwortlichen Administratoren alle sicherheitsrelevanten Einstellungen konfigurieren und sichere Werte einsetzen. Hierbei stützen sie sich auf Sicherheitsexperten, die die sicherheitsrelevanten Einstellungen identifizieren und in Sicherheitskonfigurationsrichtlinien dokumentieren. Das Identifizieren der sicherheitsrelevanten Einstellungen ist allerdings zeitaufwändig und teuer, weshalb ihm oft keine Priorität beigemessen wird. Um dieses Problem zu lösen, nutzen wir aktuelle Verfahren der Computerlinguistik, um Einstellungen auf der Grundlage ihrer Beschreibung in natürlicher Sprache als sicherheitsrelevant zu klassifizieren. Allerdings zeigt unsere Evaluation, dass die trainierten Klassifikatoren nicht gut genug sind, um die menschlichen Sicherheitsexperten vollständig zu ersetzen sondern höchstens bei der Klassifizierung der Einstellungen helfen können. Durch die Veröffentlichung unserer gelabelten Datensätze und all unserer Modelle wollen wir Sicherheitsexperten bei der Analyse von Konfigurationseinstellungen unterstützen und weitere Forschung in diesem Bereich ermöglichen.
  • Konferenzbeitrag
    BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing - Summary
    (Software Engineering 2023, 2023) Nguyen, Hoang Lam; Grunske, Lars
    This paper summarizes our work ”BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing” [NG22], presented at the 44th International Conference on Software Engineering (ICSE 2022).
  • Konferenzbeitrag
    Benchmarking Scalability of Cloud-Native Applications
    (Software Engineering 2023, 2023) Henning, Sören; Hasselbring, Wilhelm
    This contribution has been published in the journal Empirical Software Engineering (Springer Nature) in 2022 [HH22], https://doi.org/10.1007/s10664-022-10162-1.
  • Konferenzbeitrag
    BXtendDSL: A layered framework for bidirectional model transformations combining a declarative and an imperative language (Summary)
    (Software Engineering 2023, 2023) Buchmann, Thomas; Bank, Matthias; Westfechtel, Bernhard
    This summary is based on an article which appeared in 2022 in The Journal of Systems & Software [BBW22]. Bidirectional transformations have been studied in a wide range of application domains. In modeldriven software engineering, they are required for roundtrip engineering processes. We present a pragmatic approach to engineering bidirectional model transformations that assists transformation developers by domain-specific languages, frameworks, and code generators. A thorough evaluation demonstrates conciseness, expressiveness, and scalability of our approach.
  • Konferenzbeitrag
    Classifying Edits to Variability in Source Code - Summary
    (Software Engineering 2023, 2023) Bittner, Paul Maximilian; Tinnes, Christof; Schultheiß, Alexander; Viegener, Sören; Kehrer, Timo; Thüm, Thomas
    We report about recent research on edit classification in configurable software, originally published at the 30th Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2022 [Bi22]. For highly configurable software systems, such as the Linux kernel, maintaining and evolving variability information along changes to source code poses a major challenge. While source code itself may be edited, also feature-to-code mappings may be introduced, removed, or changed. In practice, such edits are often conducted ad-hoc and without proper documentation. To support the maintenance and evolution of variability, it is desirable to understand the impact of each edit on the variability. We propose the first complete and unambiguous classification of edits to variability in source code by means of a catalog of edit classes. This catalog is based on a scheme that can be used to build classifications that are complete and unambiguous by construction. To this end, we introduce a complete and sound model for edits to variability. In about 21.5 ms per commit, we validate the correctness, relevance, and suitability of our classification by classifying each edit in 1.7 million commits in the change histories of 44 open-source software systems automatically.
  • Konferenzbeitrag
    Collaborative Program Comprehension in Extended Reality
    (Software Engineering 2023, 2023) Krause-Glau, Alexander; Hansen, Malte; Hasselbring, Wilhelm
    This contribution has been published in the journal Information and Software Technology (Elsevier) in 2022 [KHH22], https://doi.org/10.1016/j.infsof.2022.107007.