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Method to automatically select best reviewers based on the metadata of artifacts being reviewed and experience and disponibility of reviewers

IP.com Disclosure Number: IPCOM000238229D
Publication Date: 2014-Aug-11
Document File: 2 page(s) / 37K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are an automated system and method to determine the best reviewer of software code based on reviewer skills, experience, and availability. The method uses data about the developers, based on previous work, to select the proper reviewers for specific code or code changes.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

Page 01 of 2

Method to automatically select best reviewers based on the metadata of artifacts being reviewed and experience and disponibility of reviewers

It is more cost effective to deploy correct, working code than it is to fix broken code after it is in production. Initial good quality reduces development, installation, and testing time. A method for finding and fixing errors early in the development cycle ensures good quality and an efficient overall process. Code review, when properly executed, can identify errors before code is integrated into the product .

One issue that the code review process faces is the proper selection of the person (s) to perform the review. Currently, reviewer selection is a manual process; the person requesting the code review selects the reviewer(s) based on known experience and relevant expertise. However, this manual process is error-prone. If the manual selection does not locate the most experienced reviewer, then the development cycle and product are at risk.

The novel contribution a system and method to determine the best reviewer of software code based on reviewer skills, experience, and availability. The system automatically selects the proper reviewers based on the metadata within the content under review (e.g., code complexity) and a given reviewer's reputation (i.e. known levels of expertise)

which can be defined based on the reviewer's past work on specific projects .

The method uses data about the developers , based on previous work, to select the proper reviewers for specific code or code changes. For example, the system can gather metadata from annotations to developed or previously reviewed code that provides an indication of the developer's level of expertise. In addition, user definitions can be enhanced to add data, such as experience and expertise, about the developers defined in the system.

Implemented as software, the system checks the metadata of the item being reviewed , the background information of all available reviewers, and the historical information of the artifact management system. The reviewer's background/management system automatically learns from previous code reviews based on completed processes or manual filing.

This can also be used when reviewing requirements in a requirements management tool. Here, the user creates a list of requirements and then manually selects the reviewers from a list of all stakeholders in the project. The system extracts metadata about the re...