arXiv subjects

Natarajan Chidambaram

Publications and source records attributed to Natarajan Chidambaram.

4 recordsLinked to original sources

arXiv2026

A Two-Dimensional Study of the Model Context Protocol: Publication and Adoption

The Model Context Protocol (MCP), released by Anthropic in November 2024, standardizes how large language model applications connect to external tools and data sources. Despite MCP's rapid growth, no study has jointly characterized its emergence in the research literature, its adoption on GitHub and the relationship between them. We address this gap with a longitudinal, two-dimensional study of 802 MCP-related publications and 33,319 GitHub repositories. We characterize their growth and identify their application domains via BERTopic and usage contexts via a human-validated GPT-5.6-Luna classifier. We find that publications and repositories began rapid growth within a month of each other in 2025, and peaked together in March 2026. Notably, a second growth wave alone accounts for 49.5% of the GitHub repositories. Most publications (57.8%) and nearly all repositories (93.7%) use MCP as an enabling technology rather than for analyzing, evaluating, extending, or securing it directly. Among repositories, the latter category concentrates almost entirely in one general-purpose topic, while applied usage spreads into product-specific niches. Finally, we provide an overview of the MCP landscape and recommendations to researchers and practitioners in this field.

cs.SE↗

arXiv2024

CIA: Controllable Image Augmentation Framework Based on Stable Diffusion

Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing the existence of specific patterns in generated images using accurate prompting and ControlNet. In order to show how CIA can be used to search for an optimal augmentation pipeline of training data, we study human object detection in a data constrained scenario, using YOLOv8n on COCO and Flickr30k datasets. We have recorded significant improvement using CIA-generated images, approaching the performances obtained when doubling the amount of real images in the dataset. Our findings suggest that our modular framework can significantly enhance object detection systems, and make it possible for future research to be done on data-constrained scenarios. The framework is available at: github.com/multitel-ai/CIA.

cs.CV↗

arXiv2022

Bot Detection in GitHub Repositories

Contemporary social coding platforms like GitHub promote collaborative development. Many open-source software repositories hosted in these platforms use machine accounts (bots) to automate and facilitate a wide range of effort-intensive and repetitive activities. Determining if an account corresponds to a bot or a human contributor is important for socio-technical development analytics, for example, to understand how humans collaborate and interact in the presence of bots, to assess the positive and negative impact of using bots, to identify the top project contributors, to identify potential bus factors, and so on. Our project aims to include the trained machine learning (ML) classifier from the BoDeGHa bot detection tool as a plugin to the GrimoireLab software development analytics platform. In this work, we present the procedure to form a pipeline for retrieving contribution and contributor data using Perceval, distinguishing bots from humans using BoDeGHa, and visualising the results using Kibana.

cs.SE↗

arXiv2022

Leveraging Predictions from Multiple Repositories to Improve Bot Detection

Contemporary social coding platforms such as GitHub facilitate collaborative distributed software development. Developers engaged in these platforms often use machine accounts (bots) for automating effort-intensive or repetitive activities. Determining whether a contributor corresponds to a bot or a human account is important in socio-technical studies, for example, to assess the positive and negative impact of using bots, analyse the evolution of bots and their usage, identify top human contributors, and so on. BoDeGHa is one of the bot detection tools that have been proposed in the literature. It relies on comment activity within a single repository to predict whether an account is driven by a bot or by a human. This paper presents preliminary results on how the effectiveness of BoDeGHa can be improved by combining the predictions obtained from many repositories at once. We found that doing this not only increases the number of cases for which a prediction can be made but that many diverging predictions can be fixed this way. These promising, albeit preliminary, results suggest that the "wisdom of the crowd" principle can improve the effectiveness of bot detection tools.

cs.SE↗