In the recent years, studies of design and programming practices in mobile development are gaining more attention from researchers. Several such empirical studies used Android applications (paid, free, and open source) to analyze factors such as size, quality, dependencies, reuse, and cloning. Most of the studies use executable files of the apps (APK files), instead of source code because of availability issues (most of free apps available at the Android official market are not open-source, but still can be downloaded and analyzed in APK format). However, using only APK files in empirical studies comes with some threats to the validity of the results.In this paper, we analyze some of these pertinent threats. In particular, we analyzed the impact of third-party libraries and code obfuscation practices on estimating the amount of reuse by class cloning in Android apps. When including and excluding third-party libraries from the analysis, we found statistically significant differences in the amount of class cloning 24,379 free Android apps. Also, we found some evidence that obfuscation is responsible for increasing a number of false positives when detecting class clones. Finally, based on our findings, we provide a list of actionable guidelines for mining and analyzing large repositories of Android applications and minimizing these threats to validity.

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According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F -measure, and our results show that the algorithms can accurately classify the subjects' thoughts into corresponding action commands.

Abstract-Approaches that support software maintenance need to be evaluated and compared against existing ones, in order to demonstrate their usefulness in practice. However, oftentimes the lack of well-established sets of benchmarks leads to situations where these approaches are evaluated using different datasets, which results in biased comparisons. In this data paper we describe and make publicly available a set of benchmarks from six Java applications, which can be used in the evaluation of various software engineering (SE) tasks, such as feature location and impact analysis. These datasets consist of textual description of change requests, the locations in the source code where they were implemented, and execution traces. Four of the benchmarks were already used in several SE research papers, and two of them are new. In addition, we describe in detail the methodology used for generating these benchmarks and provide a suite of tools in order to encourage other researchers to validate our datasets and generate new benchmarks for other subject software systems. Our online appendix: http://www.cs.wm.edu/semeru/data/msr13/