Abstract
Context: Testing activities are essential for the quality assurance of mobile applications under development. Despite its importance, some studies show that testing is not widely applied in mobile applications. Some characteristics of mobile devices and a varied market of mobile devices with different operating system versions lead to a highly fragmented mobile ecosystem. Thus, researchers put some effort into proposing different solutions to optimize mobile application testing. Objective: The main goal of this paper is to provide a categorization and classification of existing testing infrastructures to support mobile application testing. Methods: To this aim, the study provides a Systematic Mapping Study of 27 existing primary studies. Results: We present a new classification and categorization of existing types of testing infrastructure, the types of supported devices and operating systems, whether the testing infrastructure is available for usage or experimentation, and supported testing types and applications. Conclusion: Our findings show a need for mobile testing infrastructures that support multiple phases of the testing process. Moreover, we showed a need for testing infrastructure for context-aware applications and support for both emulators and real devices. Finally, we pinpoint the need to make the research available to the community whenever possible.

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Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.