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Workshop Automatische Bewertung von Programmieraufgaben

https://dl.gi.de/handle/20.500.12116/27934
Siehe auch https://www.abp-workshop.de/.
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Auflistung Workshop Automatische Bewertung von Programmieraufgaben nach Schlagwort "Automated Assessment"

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  • Konferenzbeitrag
    An Architecture for the Automated Assessment of Web Programming Tasks
    (Proceedings of the Fifth Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2021),virtual event, October 28-29, 2021, 2021) Aubele, Lara; Martin, Leon; Hirmer, Tobias; Henrich, Andreas
    Automatically assessing students' solutions to programming tasks in the domain of web programming requires special means due to the characteristics of web applications. This paper proposes an architecture for a web-based learning application tailored to this domain. For the implementation of the automated assessment of programming tasks, we make use of end-to-end testing and container virtualization. This allows, in contrast to other popular approaches, the coverage of tasks that include special operations like DOM manipulations, which alter the user interface of web applications, in a way that is convenient for both students and instructors. We demonstrate the capabilities and limitations of the architecture based on two common usage scenarios.
  • Konferenzbeitrag
    Automated Detection of Bugs in Error Handling for Teaching Secure C Programming
    (Proceedings of the Sixth Workshop "Automatische Bewertung von Programmieraufgaben" (ABP 2023), 2023) Schrötter, Max; Falk, Maximilian; Schnor, Bettina
    The Low-Level programming language C is widely used for Operating Systems, Embedded Systems and other performance critical applications. Since these applications are often security critical, they require secure programming. The C language on the other hand allows novice programmers to write insecure code easily. This makes it especially important to teach secure programming and give students feedback on potential security issues. One critical bug that is often overlooked is the incorrect handling of errors. In this paper, we present an Error Handling Analyzer (EHA) for the CoFee framework. The EHA detects missing error handling and incorrect error handling using the Clang Static Analyzer. We evaluated EHA on 100 student submissions and found that error handling bugs are a common mistake and that EHA can detect more than 80 % of the error handling bugs in these submissions.