Biblio

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.

Sophistication and flexibility of software development make it easy to leave security vulnerabilities in software applications for attack- ers. It is critical to educate and train software engineers to avoid in- troducing vulnerabilities in software applications in the first place such as adopting secure coding mechanisms and conducting secu- rity testing. A number of websites provide training grounds to train people’s hacking skills, which are highly related to security test- ing skills, and train people’s secure coding skills. However, there exists no interactive gaming platform for instilling gaming aspects into the education and training of secure coding. To address this issue, we propose to construct secure coding duels in Code Hunt, a high-impact serious gaming platform released by Microsoft Re- search. In Code Hunt, a coding duel consists of two code segments: a secret code segment and a player-visible code segment. To solve a coding duel, a player iteratively modifies the player-visible code segment to match the functional behaviors of the secret code seg- ment. During the duel-solving process, the player is given clues as a set of automatically generated test cases to characterize sample functional behaviors of the secret code segment. The game aspect in Code Hunt is to recognize a pattern from the test cases, and to re-engineer the player-visible code segment to exhibit the expected behaviors. Secure coding duels proposed in this work are coding duels that are carefully designed to train players’ secure coding skills, such as sufficient input validation and access control.

Applications in mobile marketplaces may leak private user information without notification. Existing mobile platforms provide little information on how applications use private user data, making it difficult for experts to validate appli- cations and for users to grant applications access to their private data. We propose a user-aware-privacy-control approach, which reveals how private information is used inside applications. We compute static information flows and classify them as safe/un- safe based on a tamper analysis that tracks whether private data is obscured before escaping through output channels. This flow information enables platforms to provide default settings that expose private data for only safe flows, thereby preserving privacy and minimizing decisions required from users. We build our approach into TouchDe- velop, an application-creation environment that allows users to write scripts on mobile devices and install scripts published by other users. We evaluate our approach by studying 546 scripts published by 194 users, and the results show that our approach effectively reduces the need to make access-granting choices to only 10.1 % (54) of all scripts. We also conduct a user survey that involves 50 TouchDevelop users to assess the effectiveness and usability of our approach. The results show that 90 % of the users consider our approach useful in protecting their privacy, and 54 % prefer our approach over other privacy-control approaches.