Biblio

While there have been various studies identifying and classifying Android malware, there is limited discussion of the broader class of apps that fall in a gray area. Mobile grayware is distinct from PC grayware due to differences in operating system properties. Due to mobile grayware’s subjective nature, it is difficult to identify mobile grayware via program analysis alone. Instead, we hypothesize enhancing analysis with text analytics can effectively reduce human effort when triaging grayware. In this paper, we design and implement heuristics for seven main categories of grayware.We then use these heuristics to simulate grayware triage on a large set of apps from Google Play. We then present the results of our empirical study, demonstrating a clear problem of grayware. In doing so, we show how even relatively simple heuristics can quickly triage apps that take advantage of users in an undesirable way.
 

In recent years, online programming and software engineering education via information technology has gained a lot of popularity. Typically, popular courses often have hundreds or thousands of students but only a few course sta members. Tool automation is needed to maintain the quality of education. In this paper, we envision that the capability of quantifying behavioral similarity between programs is helpful for teaching and learning programming and software engineering, and propose three metrics that approximate the computation of behavioral similarity. Speci cally, we leverage random testing and dynamic symbolic execution (DSE) to generate test inputs, and run programs on these test inputs to compute metric values of the behavioral similarity. We evaluate our metrics on three real-world data sets from the Pex4Fun platform (which so far has accumulated more than 1.7 million game-play interactions). The results show that our metrics provide highly accurate approximation to the behavioral similarity. We also demonstrate a number of practical applications of our metrics including hint generation, progress indication, and automatic grading.