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Development Effort Estimation and Resource Prediction using App Store Review Analytics

IP.com Disclosure Number: IPCOM000247833D
Publication Date: 2016-Oct-06
Document File: 3 page(s) / 70K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method to use publicly available information related to an application, specifically regarding present defects or suggested enhancements, in order to accurately predict app development resource allocation and estimate the required effort to repair defects and implement enhancements.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 47% of the total text.

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Development Effort Estimation and Resource Prediction using App Store Review Analytics

Development effort estimation and resource prediction for application (app) development is a common problem in software development. Hundreds of mobile applications are released in the market every month. With this high volume, app development companies need to have a sophisticated means for identifying app defects, finding opportunities for new enhancements, and understanding the costs involved in addressing those defects and enhancements in order to prioritize work.

App users provide reviews of the apps in the app stores as well technical discussions in online forums. App development companies need to monitor and analyze the reviews, comments, and discussions in multiple forums in order to prioritize work to repair defects or implement enhancements. Defects and enhancements are entered into project and defect tracking systems and are often categorized based on functionality, performance, and usability.

Based on the number of work items (i.e., defects and enhancements), project managers need to allocate development resources. For example, performance related defects require more performance engineers. Estimating the effort needed to fix the defects in each category and then predicting the resources that need to be allocated for each category of the defects are challenging tasks for project managers.

Typically, effort estimation is based on historical data available in the defect and project tracking systems.

App-store reviews and social media commentary are freely available to access for users and developers. A method is needed to leverage this valuable data to predict development resource allocation with various specializations and estimate the effort required in the future for a project.

The novel contribution is a method to use publicly available information related to the app in question in order to accurately predict app development resource allocation and estimate the required effort.

Figure: High-level design*

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The novel method mines app-store reviews of mobile apps and reports effort estimation and resource allocation required to resolve the defects mentioned in the app-store reviews using the following steps:

Step 1: Software continuously analyzes app-store reviews, online forums, etc., and mines defects from the text. Each mined defect includes the following details:


A. Defect description


B. Defect Type (Performance, Functional and Usability


C. Defect correlation


D. Defect criticality

Step 2: System detects defects labelled with defect type (e.g., performance/usability/functional) and criticality. These details are entered into defect and project tracking systems. These defect and project tracking systems are used by an organization for development planning and defect tracking and have information about the actual effort utilized for fixing defects that were either discovered during internal testing or reported by custo...