Bugs in Scratch programs can spoil the fun and inhibit learning success. Many common bugs are the result of recurring patterns of bad code. In this paper we present a collection of common code patterns that typically hint at bugs in Scratch programs, and the LitterBox tool which can automatically detect them. We empirically evaluate how frequently these patterns occur, and how severe their consequences usually are. While fixing bugs inevitably is part of learning, the possibility to identify the bugs automatically provides the potential to support learners. CCS CONCEPTS• Social and professional topics → Software engineering education; K-12 education; • Software and its engineering → Visual languages.

Creating programs with block-based programming languages like Sc r a t c h is easy and fun. Block-based programs can nevertheless contain bugs, in particular when learners have misconceptions about programming. Even when they do not, Sc r a t c h code is often of low quality and contains code smells, further inhibiting understanding, reuse, and fun. To address this problem, in this paper we introduce Lit t e r Bo x , a linter for Sc r a t c h programs. Given a program or its public project ID, Lit t e r Bo x checks the program against patterns of known bugs and code smells. For each issue identified, Lit t e r Bo x provides not only the location in the code, but also a helpful explanation of the underlying reason and possible misconceptions. Learners can access Lit t e r Bo x through an easy to use web interface with visual information about the errors in the block-code, while for researchers Lit t e r Bo x provides a general, open source, and extensible framework for static analysis of Sc r a t c h programs.

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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For teachers, automated tool support for debugging and assessing their students' programming assignments is a great help in their everyday business. For block-based programming languages which are commonly used to introduce younger learners to programming, testing frameworks and other software analysis tools exist, but require manual work such as writing test suites or formal specifications. However, most of the teachers using languages like SCRATCH are not trained for or experienced in this kind of task. Linters do not require manual work but are limited to generic bugs and therefore miss potential task-specific bugs in student solutions. In prior work, we proposed the use of anomaly detection to find project-specific bugs in sets of student programming assignments automatically, without any additional manual labour required from the teachers' side. Evaluation on student solutions for typical programming assignments showed that anomaly detection is a reliable way to locate bugs in a data set of student programs. In this paper, we enhance our initial approach by lowering the abstraction level. The results suggest that the lower abstraction level can focus anomaly detection on the relevant parts of the programs.