Mobile systems commonly support an event-based model of concurrent programming. This model, used in popular platforms such as Android, naturally supports mobile devices that have a rich array of sensors and user input modalities. Unfortunately, most existing tools for detecting concurrency errors of parallel programs focus on a thread-based model of concurrency. If one applies such tools directly to an event-based program, they work poorly because they infer false dependencies between unrelated events handled sequentially by the same thread.In this paper we present a race detection tool named CAFA for event-driven mobile systems. CAFA uses the causality model that we have developed for the Android event-driven system. A novel contribution of our model is that it accounts for the causal order due to the event queues, which are not accounted for in past data race detectors. Detecting races based on low-level races between memory accesses leads to a large number of false positives. CAFA overcomes this problem by checking for races between high-level operations. We discuss our experience in using CAFA for finding and understanding a number of known and unknown harmful races in open-source Android applications.

Several program analysis tools-such as plagiarism detection and bug finding-rely on knowing a piece of code's relative semantic importance. For example, a plagiarism detector should not bother reporting two programs that have an identical simple loop counter test, but should report programs that share more distinctive code. Traditional program analysis techniques (e.g., finding data and control dependencies) are useful, but do not say how surprising or common a line of code is. Natural language processing researchers have encountered a similar problem and addressed it using an n-gram model of text frequency, derived from statistics computed over text corpora.We propose and compute an n-gram model for programming languages, computed over a corpus of 2.8 million JavaScript programs we downloaded from the Web. In contrast to previous techniques, we describe a code n-gram as a subgraph of the program dependence graph that contains all nodes and edges reachable in n steps from the statement. We can count n-grams in a program and count the frequency of n-grams in the corpus, enabling us to compute tf-idf-style measures that capture the differing importance of different lines of code. We demonstrate the power of this approach by implementing a plagiarism detector with accuracy that beats previous techniques, and a bug-finding tool that discovered over a dozen previously unknown bugs in a collection of real deployed programs.

Mobile systems commonly support an event-based model of concurrent programming. This model, used in popular platforms such as Android, naturally supports mobile devices that have a rich array of sensors and user input modalities. Unfortunately, most existing tools for detecting concurrency errors of parallel programs focus on a thread-based model of concurrency. If one applies such tools directly to an event-based program, they work poorly because they infer false dependencies between unrelated events handled sequentially by the same thread.In this paper we present a race detection tool named CAFA for event-driven mobile systems. CAFA uses the causality model that we have developed for the Android event-driven system. A novel contribution of our model is that it accounts for the causal order due to the event queues, which are not accounted for in past data race detectors. Detecting races based on low-level races between memory accesses leads to a large number of false positives. CAFA overcomes this problem by checking for races between high-level operations. We discuss our experience in using CAFA for finding and understanding a number of known and unknown harmful races in open-source Android applications.

Arecoline is known to induce reactive oxygen species (ROS). Our previous studies showed that arecoline inhibited myogenic differentiation and acetylcholine receptor cluster formation of C2C12 myoblasts. N-acetyl-cysteine (NAC) is a known ROS scavenger. We hypothesize that NAC scavenges the excess ROS caused by arecoline. In this article we examined the effect of NAC on the inhibited myoblast differentiation by arecoline and related mechanisms. We found that NAC less than 2 mM is non-cytotoxic to C2C12 by viability analysis. We further demonstrated that NAC attenuated the decreased number of myotubes and nuclei in each myotube compared to arecoline treatment by H & E staining. We also showed that NAC prevented the decreased expression level of the myogenic markers, myogenin and MYH caused by arecoline, using immunocytochemistry and western blotting. Finally, we found that NAC restored the decreased expression level of p-ERK1/2 by arecoline. In conclusion, our results indicate that NAC attenuates the damage of the arecoline-inhibited C2C12 myoblast differentiation by the activation/phosphorylation of ERK. This is the first report to demonstrate that NAC has beneficial effects on skeletal muscle myogenesis through ERK1/2 upon arecoline treatment. Since defects of skeletal muscle associates with several diseases, NAC can be a potent drug candidate in diseases related to defects in skeletal muscle myogenesis.

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