Common Bugs in Scratch Programs

Frädrich, Christoph; Obermüller, Florian; Körber, Nina; Heuer, Ute

doi:10.1145/3341525.3387389

Cited by 35 publications

(57 citation statements)

References 19 publications

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“…Our tool chain for anomaly detection for SCRATCH uses an extended version of Li t t e r Bo x [10] to generate a collection (mi , . .…”

Section: Methodsmentioning

confidence: 99%

“…We therefore conducted a sensitivity analysis on these two parameters with minimum size (= 2) and maximum deviation level (= 10000) as fixed variables, changing only minimum support and minimum confidence. For the minimum support we tested the values (1,5,10,15,20), where 20 is the default JADET value. For confidence we tested the values ( 0 .…”

Section: Methodsmentioning

confidence: 99%

“…• Bug pattern (defective): The violation hints at a defect that a generic SCRATCH linter such as Lit t e r Bo x [10] could find equally well.…”

Section: Methodsmentioning

confidence: 99%

“…It has been shown that various types of code smells are prevalent [1], [17], [36], [40] and have a negative impact on code understanding [16]. There are tools for finding code smells in Sc r a t c h programs such as Ha ir b a l l [5], Qu a l it y h o u n d [40] or SAT [6], and Li t t e r Bo x [10] detects predefined bug patterns automatically.…”

Section: Program Analysis For Scratchmentioning

confidence: 99%

“…We implemented our approach as an extension of Lit t e r -Bo x [10] and JADET [42], [44] and it is available at: https://github.com/se2p/scratch-anomalies…”

Section: E Applicationmentioning

confidence: 99%

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Finding Anomalies in Scratch Assignments

Körber

Geldreich

Stahlbauer

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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In programming education, teachers need to monitor and assess the progress of their students by investigating the code they write. Code quality of programs written in traditional programming languages can be automatically assessed with automated tests, verification tools, or linters. In many cases these approaches rely on some form of manually written formal specification to analyze the given programs. Writing such specifications, however, is hard for teachers, who are often not adequately trained for this task. Furthermore, automated tool support for popular block-based introductory programming languages like SCRATCH is lacking. Anomaly detection is an approach to automatically identify deviations of common behavior in datasets without any need for writing a specification. In this paper, we use anomaly detection to automatically find deviations of SCRATCH code in a classroom setting, where anomalies can represent erroneous code, alternative solutions, or distinguished work. Evaluation on solutions of different programming tasks demonstrates that anomaly detection can successfully be applied to tightly specified as well as open-ended programming tasks.

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“…Our tool chain for anomaly detection for SCRATCH uses an extended version of Li t t e r Bo x [10] to generate a collection (mi , . .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…• Bug pattern (defective): The violation hints at a defect that a generic SCRATCH linter such as Lit t e r Bo x [10] could find equally well.…”

Section: Methodsmentioning

confidence: 99%

Section: Program Analysis For Scratchmentioning

confidence: 99%

“…We implemented our approach as an extension of Lit t e r -Bo x [10] and JADET [42], [44] and it is available at: https://github.com/se2p/scratch-anomalies…”

Section: E Applicationmentioning

confidence: 99%

See 3 more Smart Citations

Finding Anomalies in Scratch Assignments

Körber

Geldreich

Stahlbauer

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)

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Search-Based Testing for Scratch Programs

Deiner

Frädrich

Geserer

et al. 2020

Search-Based Software Engineering

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Automated test generation for Scratch programs

et al. 2023

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The importance of programming education has led to dedicated educational programming environments, where users visually arrange block-based programming constructs that typically control graphical, interactive game-like programs. The Scratch programming environment is particularly popular, with more than 90 million registered users at the time of this writing. While the block-based nature of Scratch helps learners by preventing syntactical mistakes, there nevertheless remains a need to provide feedback and support in order to implement desired functionality. To support individual learning and classroom settings, this feedback and support should ideally be provided in an automated fashion, which requires tests to enable dynamic program analysis. In prior work we introduced Whisker, a framework that enables automated testing of Scratch programs. However, creating these automated tests for Scratch programs is challenging. In this paper, we therefore investigate how to automatically generate Whisker tests. Generating tests for Scratch raises important challenges: First, game-like programs are typically randomised, leading to flaky tests. Second, Scratch programs usually consist of animations and interactions with long delays, inhibiting the application of classical test generation approaches. Thus, the new application domain raises the question of which test generation technique is best suited to produce high coverage tests capable of detecting faulty behaviour. We investigate these questions using an extension of the Whisker test framework for automated test generation. Evaluation on common programming exercises, a random sample of 1000 Scratch user programs, and the 1000 most popular Scratch programs demonstrates that our approach enables Whisker to reliably accelerate test executions, and even though many Scratch programs are small and easy to cover, there are many unique challenges for which advanced search-based test generation using many-objective algorithms is needed in order to achieve high coverage.

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Common Bugs in Scratch Programs

Cited by 35 publications

References 19 publications

Finding Anomalies in Scratch Assignments

Finding Anomalies in Scratch Assignments

Search-Based Testing for Scratch Programs

Automated test generation for Scratch programs

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