Empowering global software development with business intelligence

Context: Global Software Development (GSD) allows companies to take advantage of talent spread across the world. Most research has been focused on the development aspect. However, little if any attention has been paid to the management of GSD projects. Studies report a lack of adequate support for management’s decisions made during software development, further accentuated in GSD since information is scattered throughout multiple factories, stored in different formats and standards. Objective: This paper aims to improve GSD management by proposing a systematic method for adapting Business Intelligence techniques to software development environments. This would enhance the visibility of the development process and enable software managers to make informed decisions regarding how to proceed with GSD projects. Method: A combination of formal goal-modeling frameworks and data modeling techniques is used to elicitate the most relevant aspects to be measured by managers in GSD. The process is described in detail and applied to a real case study throughout the paper. A discussion regarding the generalisability of the method is presented afterwards. Results: The application of the approach generates an adapted BI framework tailored to software development according to the requirements posed by GSD managers. The resulting framework is capable of presenting previously inaccessible data through common and specific views and enabling data navigation according to the organization of software factories and projects in GSD. Conclusions: We can conclude that the proposed systematic approach allows us to successfully adapt Business Intelligence techniques to enhance GSD management beyond the information provided by traditional tools. The resulting framework is able to integrate and present the information in a single place, thereby enabling easy comparisons across multiple projects and factories and providing support for informed decisions in GSD management.

This work has partially funded by the GEODAS-BI (TIN2012- 37493-C03-03) and GEODAS-BC (TIN2012-37493-C03-01) projects from the Ministry of Economy and Competitiveness (MINECO) and the Fondo Europeo de Desarrollo Regional FEDER, SDGear (TSI- 100104-2014-4, ITEA 2-Call 7, co-funded by the Ministerio de Industria, Energía y Turismo dentro del Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2013-2016, and the APOSTD grant (APOSTD/2014/064) from the Generalitat Valenciana .