Forecasting software indicators: an industry-academia collaboration

Abstract

Context: Nowadays software-development organizations are urged to exploit their data for empowering their decision-making processes. Such data may be used to monitor the status of meaningful software indicators (e.g., software quality, productivity and on-time delivery) that are relevant for their decision-making processes. Forecasting the values of such indicators may provide evidence of a potentially high risk or opportunity that could help to anticipate actions accordingly. Most of the existing forecasting proposals in software engineering use open-source data rather than data from industrial projects. Therefore, there is a lack of evidence on how these proposals ft the particular needs of a software-development organization and how they can be automated into the organization’s infrastructure. Objective: To enable software indicators´ forecasting in a software-development organization (Modeliosoft). Method: We designed an industry-academia collaboration based on Action Design Research (ADR) to address Modeliosoft’s forecasting challenges. Results A tool-supported method called FOSI (Forecasting Of Software Indicators) for enabling forecasting in Modeliosoft. We obtained positive results regarding its suitability and technical feasibility in a pilot project of the organization. In addition, we provide details and refections on the potential usefulness of the method for addressing similar feld problems. Conclusions: The procedures and results detailed in this paper are valuable to: 1) address Modeliosoft’s forecasting challenges 2) inspire other software-development organizations on how to deal with similar problems and even reuse some procedures and software support tools resulted from this work, 3) promote the win-win benefts of industry-academia collaborations.