18186458. GENERATING REVIEW LIKELIHOODS FOR SETS OF CODE simplified abstract (ADOBE INC.)
Contents
GENERATING REVIEW LIKELIHOODS FOR SETS OF CODE
Organization Name
Inventor(s)
Charles Robert John Matthews of Edinburgh (GB)
Thomas Stanley Dalton of St Andrews (GB)
Harinder Singh Sandhu of Edinburgh (GB)
David Alexander Collie of Edinburgh (GB)
Adrian John O'lenskie of Dunblane (GB)
GENERATING REVIEW LIKELIHOODS FOR SETS OF CODE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18186458 titled 'GENERATING REVIEW LIKELIHOODS FOR SETS OF CODE
The abstract describes a patent application for a system that generates review likelihoods for sets of code based on input data compiled from code data and reviewer data. The system uses a machine learning model to generate review likelihoods for potential reviewers of new code, which are then displayed in a user interface.
- The system compiles input data from code data and reviewer data.
- A machine learning model is used to generate review likelihoods for potential reviewers of new code.
- The review likelihoods are displayed in a user interface for selection of reviewers.
- The system aims to improve the review process for sets of code by predicting suitable reviewers.
- By utilizing machine learning, the system enhances the efficiency and accuracy of the review process.
Potential Applications: - Software development companies can use this system to streamline their code review process. - Open-source projects can benefit from more efficient reviewer selection. - Quality assurance teams can improve the effectiveness of their code reviews.
Problems Solved: - Inefficient reviewer selection process. - Lack of accuracy in predicting suitable reviewers for code sets. - Time-consuming manual review processes.
Benefits: - Increased efficiency in code review processes. - Improved accuracy in selecting suitable reviewers. - Enhanced collaboration and communication among development teams.
Commercial Applications: Title: "Enhanced Code Review System for Efficient Reviewer Selection" This technology can be applied in software development companies, open-source projects, and quality assurance teams to optimize the code review process, leading to improved productivity and code quality.
Prior Art: Researchers can explore prior art related to machine learning models for code review systems to understand the existing technology landscape in this field.
Frequently Updated Research: Stay updated on advancements in machine learning models for code review systems to ensure the implementation of the latest technologies in this domain.
Questions about the technology: 1. How does this system improve the efficiency of the code review process? - This system enhances efficiency by predicting suitable reviewers for sets of new code, streamlining the reviewer selection process. 2. What are the key benefits of using a machine learning model for generating review likelihoods? - The use of a machine learning model improves the accuracy of reviewer selection and enhances the overall effectiveness of the code review process.
Original Abstract Submitted
In implementations of systems for generating review likelihoods for sets of code, a computing device implements a review system to compile input data based on code data describing information associated with a set of new code to be incorporated into a set of existing code and reviewer data describing information associated with a potential reviewer of sets of code. The review system processes the input data using a machine learning model trained on training data to generate review likelihoods for potential reviewers of sets of code to be selected to review sets of new code. A review likelihood for the potential reviewer of sets of code to be selected to review the set of new code is generated using the machine learning model based on processing the input data. The review system generates an indication of the review likelihood for display in a user interface.
