Applying Machine Learning for Automatic User Story Categorization in Mobile Enterprises Application Development

Applying Machine Learning for Automatic User Story Categorization in Mobile Enterprises Application Development

Mobile enterprise applications

Text classification

User stories

Mobile enterprise applications (apps) are developed in dynamic and complex environments. Hardware characteristics, operating systems and development tools are constantly changing. In larger institutions, comprehensive corporate guidelines and requirements have to be followed. In addition, larger enterprises often develop numerous apps and lack an overview of development projects. Because of the size of such companies, a comprehensive direct information exchange between developers is often not practicable. In this situation, IT support is necessary, for example to prevent unnecessary duplication of work in the development of software artifacts such as user stories, app screen designs or code features within the company. One approach to overcome these challenges is to support reusing results from previous projects by building systems to organize and analyse the knowledge base of enterprise app development projects. For such systems an automatic categorization of artifacts is required. In this work we propose using a machine learning approach to categorize user stories. The approach is evaluated on a set of user stories from real-world mobile enterprise application development projects. The results are promising and suggest that machine learning approaches can be beneficially applied to user story classification in large companies.

Jurisch, Matthias Heinrich Friedrich

Böhm, Stephan

James-Schulz, Toby

International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 15 (2020); pp. 81-94

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2020-09-11

Copyright (c) 2020 Matthias Heinrich Friedrich Jurisch, Stephan Böhm, Toby James-Schulz

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article