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Automatic Resource Assignment for Issue Resolution

IP.com Disclosure Number: IPCOM000255240D
Publication Date: 2018-Sep-11
Document File: 7 page(s) / 694K

Publishing Venue

The IP.com Prior Art Database

Abstract

Issue tracking systems are widely issued for managing the reporting and addressing of various issues in a software system. In many cases, determining the specific software components connected to the issue and the appropriate persons to resolve the issue is not straightforward and requires a series of assignments until the right components and persons are assigned to understand and resolve the issue. The techniques of this disclosure employ a machine learning model trained on existing labeled data from an issue tracking system to automate the process of assigning appropriate components to issues and routing them to personnel most suitable for handling them. Additionally, the model allocates a priority for each issue and reroutes issues in case the initial allocation fails to resolve the issue within reasonable time. KEYWORDS ● Issue tracking ● Bug tracking ● Resource allocation ● Automated issue routing ● Bug reports

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Technical Disclosure Commons

Defensive Publications Series

August 30, 2018

Automatic Resource Assignment for Issue Resolution Steven James Ross

Christopher Farrar

Follow this and additional works at: https://www.tdcommons.org/dpubs_series

This work is licensed under a Creative Commons Attribution 4.0 License. This Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.

Recommended Citation Ross, Steven James and Farrar, Christopher, "Automatic Resource Assignment for Issue Resolution", Technical Disclosure Commons, (August 30, 2018) https://www.tdcommons.org/dpubs_series/1479

Automatic resource assignment for issue resolution

ABSTRACT

Issue tracking systems are widely issued for managing the reporting and addressing of

various issues in a software system. In many cases, determining the specific software

components connected to the issue and the appropriate persons to resolve the issue is not

straightforward and requires a series of assignments until the right components and persons are

assigned to understand and resolve the issue. The techniques of this disclosure employ a machine

learning model trained on existing labeled data from an issue tracking system to automate the

process of assigning appropriate components to issues and routing them to personnel most

suitable for handling them. Additionally, the model allocates a priority for each issue and

reroutes issues in case the initial allocation fails to resolve the issue within reasonable time.

KEYWORDS

● Issue tracking

● Bug tracking

● Resource allocation

● Automated issue routing

● Bug reports

BACKGROUND

In software development, issue tracking systems are widely issued for managing the

reporting and addressing of various issues, such as bugs, desired features, customer-reported

problems, etc. In many cases, determining the specific software component(s) connected to the

issue and the appropriate person(s) to resolve the issue is not straightforward and may require a

series of assignments until the right components and persons are assigned. Such manual issue

2

Ross and Farrar: Automatic Resource Assignment for Issue Resolution

Published by Technical Disclosure Commons, 2018

routing flow that often requires multiple tries prior to reaching the correct assignment requires

substantial time and effort on the part of the person filing the issue and the persons involved in

examining and routing, thus wasting resources.

DESCRIPTION

The techniques of this disclosure operate within any system that tracks issues, such as a

bug tracking system for software (and/or hardware). The system accepts manual or automated

issue reports that include various details pertinent to the issue, such as a text description of the

problem with relevant links, logs, and other automatically generated data associated with the

issue along with corresponding timestam...