With the ever-increasing popularity of mobile devices over the last decade, mobile applications and the frameworks upon which they are built frequently change, leading to a confusing jumble of devices and applications utilizing differing features even within the same framework. For Android apps and devices—the largest such framework and marketplace— mismatches between the version of the app API installed on a device and the version targeted by the developers of an app running on that device can lead to run-time crashes, providing a poor user experience. This paper presents SAINTDroid, a holistic compatibility analysis approach that seamlessly examines both the application code and the framework code by gradually loading and analyzing classes as needed during the compatibility analysis to enable efficient and scalable identification of various types of crash-leading Android compatibility issues. We applied SAINTDroid to 3,590 real-world apps and compared the analysis results against the state-of-the-art techniques, which corroborates that SAINTDroid is up to 76% more successful in detecting compatibility issues while issuing significantly fewer false alarms. The experimental results also show that SAINTDroid is remarkably (up to 8.3 times and four times on average) faster than the state-of-the-art techniques.

Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counter example showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality, and building family based techniques for both analysis and testing. However, there has been little work on building an end to end dependability case for a software product line (where a property is modeled, a counter example is found and then validated as a true positive via testing), and none that we know of in an emerging safety-critical domain, that of robotic surgery. In this paper, we study a family of surgical robots, that combine hardware and software, and are highly configurable, representing over 1300 unique robots. At the same time, they are considered safety-critical and should have associated dependability cases. We perform a case study to understand how we can bring together lightweight formal analysis, feature modeling, and testing to provide an end to end pipeline to find potential violations of important safety properties. In the process, we learned that there are some interesting and open challenges for the research community, which if solved will lead towards more dependable safety-critical cyber-physical systems.

Small RNA sequencing is the most widely used tool for microRNA (miRNA) discovery, and shows great potential for the efficient study of miRNA cross-species transport, i.e., by detecting the presence of exogenous miRNA sequences in the host species. Because of the increased appreciation of dietary miRNAs and their far-reaching implication in human health, research interests are currently growing with regard to exogenous miRNAs bioavailability, mechanisms of cross-species transport and miRNA function in cellular biological processes. In this article, we present microRNA Discovery (miRDis), a new small RNA sequencing data analysis pipeline for both endogenous and exogenous miRNA detection. Specifically, we developed and deployed a Web service that supports the annotation and expression profiling data of known host miRNAs and the detection of novel miRNAs, other noncoding RNAs, and the exogenous miRNAs from dietary species. As a proof-of-concept, we analyzed a set of human plasma sequencing data from a milk-feeding study where 225 human miRNAs were detected in the plasma samples and 44 show elevated expression after milk intake. By examining the bovine-specific sequences, data indicate that three bovine miRNAs (bta-miR-378, -181* and -150) are present in human plasma possibly because of the dietary uptake. Further evaluation based on different sets of public data demonstrates that miRDis outperforms other state-of-the-art tools in both detection and quantification of miRNA from either animal or plant sources. The miRDis Web server is available at: http://sbbi.unl.edu/miRDis/index.php.