Vijay Walunj

Vijay Walunj
University of Missouri - Kansas City | UMKC · Ph.D. Program in Computer Science

Doctor of Philosophy
Looking to broaden my research community connect and contribute to conferences/journals as reviewers/TPC.

About

15
Publications
13,338
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
144
Citations
Introduction
Vijay Walunj is a Ph.D. student in his final-year at the University of Missouri-Kansas City SCE and engineering leader at Teladoc Health R&D. His primary research focus is the intersection of Software Engineering and AI. He is applying AI to various software engineering tasks to increase effectiveness and efficiency and developing tools/frameworks for better AI Software Development. Vijay has vast experience in architecting, designing, developing, and implementing large-scale applications.

Publications

Publications (15)
Article
Full-text available
Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that...
Article
Full-text available
Understanding software evolution is essential for software development tasks, including debugging, maintenance, and testing. As a software system evolves, it grows in size and becomes more complex, hindering its comprehension. Researchers proposed several approaches for software quality analysis based on software metrics. One of the primary practic...
Preprint
Full-text available
Understanding software evolution is essential for software development tasks, including debugging, maintenance, and testing. As a software system evolves, it grows in size and becomes more complex, hindering its comprehension. Researchers proposed several approaches for software quality analysis based on software metrics. One of the primary practic...
Article
Full-text available
Deep learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models–an experimental, iterative process that produces tens to hundreds of models before arriving at a satisfactory result. While there has been a surge in the number of tools an...
Preprint
Full-text available
Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes tha...
Conference Paper
Full-text available
Network Slicing will play a vital role in enabling a multitude of 5G applications, use cases, and services. Network slicing functions will provide an end-to-end isolation between slices with an ability to customize each slice based on the service demands (bandwidth, coverage, security, latency, reliability, etc.). Maintaining isolation of resources...
Conference Paper
Full-text available
Understanding software evolution is an imperative prerequisite for software related activities such as testing, debugging, and maintenance. As a software system evolves, it increases in size and complexity, introducing new challenges of understating the inner system interactions and subsequently hinders the overall system comprehension. While tools...
Conference Paper
Full-text available
Existing cellular communications and the upcoming 5G mobile network requires meeting high-reliability standards, very low latency, higher capacity, more security, and high-speed user connectivity. Mobile operators are looking for a programmable solution that will allow them to accommodate multiple independent tenants on the same physical infrastruc...
Conference Paper
Full-text available
Developing a deep learning model is an iterative, experimental process that produces tens to hundreds of models and artifacts before arriving at a satisfactory result. While there has been a surge in the number of software systems that aim to facilitate deep learning, the process of managing the models and their artifacts is still surprisingly chal...
Conference Paper
Full-text available
Developing an efficient Deep Learning model is an ad-hoc, iterative process based on conducting a large number of experiments. The key aspect in Deep Learning is that it can fit a particular model to a large dataset automatically. Therefore, changes in the dataset can greatly affect the underlying model performance. Subsequently, a model that is tr...
Conference Paper
Full-text available
Deep Learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development and training of deep learning models, also known as deep neural networks (DNN). Often, developing a DNN is an ad-hoc, iterative process that results in producing tens to hundreds of models before arriving at a...

Network

Cited By

Projects

Project (1)
Project
Prediction of software defects is an ongoing research topic. Prediction of defects has risen with software growth. We are building a curated dataset of portrait divergence software metrics with additional 22 metrics.