Many developers and organizations implement apps for Android, the most widely used operating system for mobile devices. Common problems developers face are the various hardware devices, customized Android variants, and frequent updates, forcing them to implement workarounds for the different versions and variants of Android APIs used in practice. In this paper, we contribute the Android Compatibility checkS dataSet (AndroidCompass) that comprises changes to compatibility checks developers use to enforce workarounds for specific Android versions in their apps. We extracted 80,324 changes to compatibility checks from 1,394 apps by analyzing the version histories of 2,399 projects from the F-Droid catalog. With AndroidCompass, we aim to provide data on when and how developers introduced or evolved workarounds to handle Android incompatibilities. We hope that AndroidCompass fosters research to deal with version incompatibilities, address potential design flaws, identify security concerns, and help derive solutions for other developers, among others-helping researchers to develop and evaluate novel techniques, and Android app as well as operating-system developers in engineering their software.

Modern software development heavily relies on the reuse of functionalities through Application Programming Interfaces (APIs). However, client developers can have issues identifying the correct usage of a certain API, causing misuses accompanied by software crashes or usability bugs. Therefore, researchers have aimed at identifying API misuses automatically by comparing client code usages to correct API usages. Some techniques rely on certain API-specific graph-based data structures to improve the abstract representation of API usages. Such techniques need to compare graphs, for instance, by computing distance metrics based on the minimal graph edit distance or the largest common subgraphs, whose computations are known to be NP-hard problems. Fortunately, there exist many abstractions for simplifying graph distance computation. However, their applicability for comparing graph representations of API usages has not been analyzed. In this paper, we provide a comparison of different distance algorithms of API-usage graphs regarding correctness and runtime. Particularly, correctness relates to the algorithms’ ability to identify similar correct API usages, but also to discriminate similar correct and false usages as well as non-similar usages. For this purpose, we systematically identified a set of eight graph-based distance algorithms and applied them on two datasets of real-world API usages and misuses. Interestingly, our results suggest that existing distance algorithms are not reliable for comparing API usage graphs. To improve on this situation, we identified and discuss the algorithms’ issues, based on which we formulate hypotheses to initiate research on overcoming them.

Reproducibility and generalizability are important criteria for today’s data management society. Hence, stand-alone solutions that work well in isolation, but cannot convince at system level lead to a frustrating user experience. As a consequence, in our demo, we take the step of accelerating queries on scientific data by integrating the multi-dimensional index structure Elf into the main-memory-optimized database management system MonetDB. The overall intention is to show that the stand-alone speed ups of using Elf can also be observed when integrated into a holistic system storing scientific data sets. In our prototypical implementation, we demonstrate the performance of an Elf-backed MonetDB on the standard OLAP-benchmark, TPC-H, and the genomic multi-dimensional range query benchmark from the scientific data community. Queries can be run live on both benchmarks by the audience, while they are able to create different indexes to accelerate selection performance.