Android enforces access control checks to protect sensitive framework APIs. If not properly protected, APIs can open the door for malicious, underprivileged apps to access sensitive resources. Unfortunately, as reported by the existing literature, such access control flaws are prevalent in Android APIs, notably in those introduced by customization parties. Hence, various solutions have been proposed to detect the flaws, particularly those due to inconsistencies. The solutions can be largely divided into two categories: convergence-based techniques and probabilistic inference approaches. In this paper, we are motivated by the promising application of using code constructs - beyond convergence analysis as proposed by the recent probabilistic approaches, to recommend access control enforcement and detect inconsistencies. Specifically, we propose a deep learning-based approach that aims to automatically learn the correspondence between various code constructs and access control requirement. This task faces significant challenges, particularly due the path-sensitive nature of Android access control implementation. To this end, we develop a static analysis pipeline that extracts and abstracts an API's implementation to succinct execution traces that can be correlated with access control labels. We then employ the statically derived features to fine-tune CodeBERT for our access control recommendation task. The fine-tuned model achieves an accuracy of 91 %, pre-cision of 91 %, and recall of 92 % on AOSP data. Additionally, our evaluation on custom ROMs shows that the model is able to rediscover previously reported inconsistencies, and even discover new ones. Hence, demonstrating its complementary nature to the existing access control evaluation and recom-mendation systems.

Covid-19 has been causing severe loss to the human race. Considering the mode of spread and severity, it is essential to make it a habit to follow various safety precautions such as using sanitizers and masks and maintaining social distancing to prevent the spread of Covid-19. Individuals are widely educated about the safety measures against the disease through various modes such as announcements through online or physical awareness campaigns, advertisements in the media, and so on. The younger generations today spend considerably more time on mobile phones and games. However, there are very few applications or games aimed to help in practicing safety measures against a pandemic, which is much lesser in the case of Covid-19. Hence, we propose a 2D survival-based game, SurviveCovid-19, aimed to educate people about safety precautions to be taken for Covid-19 outside their homes by incorporating social distancing and usage of masks and sanitizers in the game. SurviveCovid-19 has been designed as an Android-based mobile game, along with a desktop (browser) version and has been evaluated through a remote quantitative user survey, with 30 volunteers using the questionnaire based on the MEEGA+ model. The survey results are promising, with all the survey questions having a mean value greater than 3.5. The game’s quality factor was 69.3, indicating that the game could be classified as excellent quality, according to the MEEGA+ model.

Efficient representation of source code is essential for various software engineering tasks such as code classification and code clone detection. Most recent approaches for representing source code still use AST and do not leverage semantic graphs such as CFG and PDG. One effective technique for representing source code involves extracting paths from the AST and using a learning model to capture program properties. Code2vec is one such path-based approach that uses an attention-based neural network to learn code embeddings which can then be used for various downstream tasks. However, this approach uses only AST and does not leverage CFG and PDG. Even though an integrated graph approach (Code Property Graph) exists for representing source code, it has only been explored in the domain of software security. Moreover, it does not leverage the paths from the individual graphs. Our idea is to extend the path-based approach code2vec to include the semantic graphs CFG and PDG with AST, which is largely unexplored in software engineering. We evaluate our approach on the task of METHODNAMING using a C dataset of 730K methods collected from GitHub. In comparison to code2vec, our approach improves the F1 score by 11% on the full dataset and up to 100% with individual projects. We show that semantic features from the CFG and PDG paths drastically improve the performance of the software engineering tasks. We envision that looking at a mocktail of source code representations for various software engineering tasks can lay the foundation for a new line of research and a re-haul of existing research.

Game engines, are frameworks that provide a platform for developers to build games with an interface tailored to handle the complexity of game development. Though there is extensive empirical research on software frameworks, there is a need for empirical studies on game engines, as they differ from traditional software frameworks. Thus, to aid research and development in the area of game engines, we present GE526, a curated dataset of 526 game engine repositories mined from GitHub, which can help researchers to analyze game engines in terms of the release cycles, code quality, API usability and so on. To the best of our knowledge, we are not aware of any curated dataset that caters to game engines in the literature. The dataset contains metadata of all the mined repositories, including 582,079 commits, 20,138 pull requests, 30,287 issues reports and 2,111 releases. The dataset along with its documentation is made available at - https://bit.ly/3pyexnc.

Open source projects are adopting faster release cycles that reflect various changes in the software. Therefore, comprehending the effects of these changes as software architecture evolves over multiple releases becomes necessary. However, it is challenging to keep architecture in-check and add new changes simultaneously for every release. To this end, we propose a visualization tool called AC2, which allows users to examine the alterations in the architecture at both higher and lower levels of abstraction for Python projects. AC2 uses call graphs and collaboration graphs to show the interaction between different architectural components. The tool provides four different views to see the architectural changes. Users can examine two releases at a time to comprehend architectural changes between them. AC2 can support the maintainers and developers, observing changes in the project and their influence on the architecture, which allows them to examine its increasing complexity over many releases at component level. AC2 can be downloaded from https://github.com/rishalab/AC2 and the demo can be seen at https://www.youtube.com/watch?v=GNrJfZ0RCVI.