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.

No abstract

Automated testing techniques can effectively explore mobile applications in order to find faults that manifest as program crashes. A number of different techniques for automatically testing apps have been proposed and empirically compared, but previous studies focused on comparing different tools, rather than techniques. Although these studies have shown search-based approaches to be effective, it remains unclear whether superior performance of one tool compared to another is due to fundamental advantages of the underlying search technique, or due to certain engineering choices made during the implementation of the tools. In order to provide a better understanding of app testing as a search problem, we empirically study different search algorithms within the same app testing framework. Experiments on a selection of 10 nontrivial apps reveal that the costs of fitness evaluations are inhibitive, and prevent the choice of algorithm from having a major effect.

No abstract

No abstract