Big Data analytics in Agile software development: A systematic mapping study

Over the last decade, Agile methods have changed the software development process in an unparalleled way and with the increasing popularity of Big Data, optimizing development cycles through data analytics is becoming a commodity. Although a myriad of research exists on software analytics as well as...

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Bibliographic Details
Published in:Information and software technology 2021-04, Vol.132, p.106448, Article 106448
Main Authors: Biesialska, Katarzyna, Franch, Xavier, Muntés-Mulero, Victor
Format: Article
Language:English
Subjects:
Agile software development
Artificial intelligence
Data analytics
Literature review
Machine learning
Software analytics
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Summary:Over the last decade, Agile methods have changed the software development process in an unparalleled way and with the increasing popularity of Big Data, optimizing development cycles through data analytics is becoming a commodity. Although a myriad of research exists on software analytics as well as on Agile software development (ASD) practice on itself, there exists no systematic overview of the research done on ASD from a data analytics perspective. Therefore, the objective of this work is to make progress by linking ASD with Big Data analytics (BDA). As the primary method to find relevant literature on the topic, we performed manual search and snowballing on papers published between 2011 and 2019. In total, 88 primary studies were selected and analyzed. Our results show that BDA is employed throughout the whole ASD lifecycle. The results reveal that data-driven software development is focused on the following areas: code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics. As BDA and ASD are fast-developing areas, improving the productivity of software development teams is one of the most important objectives BDA is facing in the industry. This study provides scholars with information about the state of software analytics research and the current trends as well as applications in the business environment. Whereas, thanks to this literature review, practitioners should be able to understand better how to obtain actionable insights from their software artifacts and on which aspects of data analytics to focus when investing in such initiatives.
ISSN:0950-5849
1873-6025
DOI:10.1016/j.infsof.2020.106448