Improving Code Review with GitHub Issue Tracking

Software quality is an important problem for technology companies, since it substantially impacts the efficiency, usefulness, and maintainability of the final product; hence, code review is a must-do activity for software developers. During the code review process, senior engineers monitor other developers' work to spot possible problems and enforce coding standards. One of the most widely used open-source software platforms, GitHub, attracts millions of developers who use it to store their projects. This study aims to analyze code quality on GitHub from the standpoint of code reviews. We examined the code review process using GitHub's Issues Tracker, which allows team members to evaluate, discuss, and share their opinions on the proposed code before it is approved. Based on our analysis, we present a novel approach for improving the code review process by promoting regularity and community involvement.Comment: To appear in the International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2022

The Potential of Vision-Language Models for Content Moderation of Children's Videos

Natural language supervision has been shown to be effective for zero-shot learning in many computer vision tasks, such as object detection and activity recognition. However, generating informative prompts can be challenging for more subtle tasks, such as video content moderation. This can be difficult, as there are many reasons why a video might be inappropriate, beyond violence and obscenity. For example, scammers may attempt to create junk content that is similar to popular educational videos but with no meaningful information. This paper evaluates the performance of several CLIP variations for content moderation of children's cartoons in both the supervised and zero-shot setting. We show that our proposed model (Vanilla CLIP with Projection Layer) outperforms previous work conducted on the Malicious or Benign (MOB) benchmark for video content moderation. This paper presents an in depth analysis of how context-specific language prompts affect content moderation performance. Our results indicate that it is important to include more context in content moderation prompts, particularly for cartoon videos as they are not well represented in the CLIP training data.Comment: 5 pages, 1 figure. Accepted at IEEE ICMLA 202

Extracting Agent-Based Models of Human Transportation Patterns

Due to their cheap development costs and ease of deployment, surveys and questionnaires are useful tools for gathering information about the activity patterns of a large group and can serve as a valuable supplement to tracking studies done with mobile devices. However in raw form, general survey data is not necessarily useful for answering predictive questions about the behavior of a large social system. In this paper, we describe a method for generating agent activity profiles from survey data for an agent-based model (ABM) of transportation patterns of 47,000 students on a university campus. We compare the performance of our agent-based model against a Markov Chain Monte Carlo (MCMC) simulation based directly on the distributions fitted from the survey data. A comparison of our simulation results against an independently collected dataset reveals that our ABM can be used to accurately forecast parking behavior over the semester and is significantly more accurate than the MCMC estimator. © 2012 IEEE