Vijayanta Jain

Chia-Yi Su, Aakash Bansal, Vijayanta Jain et al.

This tool demonstration presents a research toolkit for a language model of Java source code. The target audience includes researchers studying problems at the granularity level of subroutines, statements, or variables in Java. In contrast to many existing language models, we prioritize features for researchers including an open and easily-searchable training set, a held out test set with different levels of deduplication from the training set, infrastructure for deduplicating new examples, and an implementation platform suitable for execution on equipment accessible to a relatively modest budget. Our model is a GPT2-like architecture with 350m parameters. Our training set includes 52m Java methods (9b tokens) and 13m StackOverflow threads (10.5b tokens). To improve accessibility of research to more members of the community, we limit local resource requirements to GPUs with 16GB video memory. We provide a test set of held out Java methods that include descriptive comments, including the entire Java projects for those methods. We also provide deduplication tools using precomputed hash tables at various similarity thresholds to help researchers ensure that their own test examples are not in the training set. We make all our tools and data open source and available via Huggingface and Github.

Parvaneh Shayegh, Vijayanta Jain, Amin Rabinia et al.

The massive growth of the Internet of Things (IoT) as a network of interconnected entities [18], brings up new challenges in terms of privacy and security requirements to the traditional software engineering domain [4]. To protect the individuals' privacy, the FTC's Fair Information Practice Principles (FIPPs) [6] proposes to companies to give notice to the consumer about their data practices, provide them with choices and give them means to have control over their own data.. Using privacy policy is the most common way for this type of notices. However, privacy policies are not generally effective due to two main reasons: first, privacy policies are long and full of legal jargon which are not understandable by a normal user; second, it is not guaranteed that an IoT device behave as it is explained in its privacy policy. In this technical report, we propose and discuss our methodologies to analyze privacy policies. By the help of this analysis, we reduce the length of a privacy policy and make it organized based on privacy practices to improve understanding level for the user. We also come up with a method to find the inconsistencies between IoT devices and their privacy policies.