ZeroIn

Sat, 01 Jan 2022 00:00:00 +0000

Our formulation of a new AI/ML-based, real-time computational framework aims to learn and flag defects in software as early as the time of commit in the developers’ repositories.

ZeroIn Network

Overview

Using novel AI/ML techniques, ZeroIn zeroes-in onto problems in software repositories and aims to identify code vulnerabilities at their very origin, namely, at the time at which developers commit their codes into their repositories.

ZeroIn: Characterizing the Data Distributions of Commits in Software Repositories

A characterization of the software development metadata is presented in terms of distributions of data that best captures the trends in the datasets, to feed into the machine learning components of ZeroIn to exploit connectivity among the sets of repositories, commits, and developers.

Kalyan Perumalla, Aradhana Soni, Rupam Dey, Steven Rich

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Using Machine Learning Towards Early Flagging of Potentially Buggy Software Commits

Using multiple classifiers we verify the feasibility of using metadata from synthetic datasets modeled by a characterization of a few large software repositories and developer profiles. Results show that the metadata-based learning approach appears promising towards early flagging of potentially buggy commits in software repositories.

Aradhana Soni, Kalyan Perumalla

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Characterizing the Distributions of Commits in Large Source Code Repositories

We present preliminary results from characterizing the distribution of 452 million commits in a metadata listing from GitHub repositories. Based on multiple distributions, we find the best fits and second best fits across different ranges in the data. The characterization is aimed at synthetic repository generation suitable for use in simulation and machine learning.

Aradhana Soni, Kalyan Perumalla, Rupam Dey

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Organization

Sponsor: Industry
Institutions: University of Tennessee, Knoxville
Period: 2021-2024

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Determining the Most Significant Metadata Features to Indicate Defective Software Commits

Tue, 23 May 2023 00:00:00 +0000

ZeroIn: Characterizing the Data Distributions of Commits in Software Repositories

Sat, 16 Apr 2022 00:00:00 +0000

Using Machine Learning Towards Early Flagging of Potentially Buggy Software Commits

Fri, 08 Apr 2022 00:00:00 +0000

Characterizing the Distributions of Commits in Large Source Code Repositories

Wed, 15 Dec 2021 00:00:00 +0000

Software | Kalyan Perumalla

ZeroIn

Overview

Related Publications

Organization

Gallery

Determining the Most Significant Metadata Features to Indicate Defective Software Commits

ZeroIn: Characterizing the Data Distributions of Commits in Software Repositories

Using Machine Learning Towards Early Flagging of Potentially Buggy Software Commits

Characterizing the Distributions of Commits in Large Source Code Repositories