On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Rique, Thiago, Perkusich, Mirko, Dantas, Emanuel, Albuquerque, Danyllo, Gorgonio, Kyller, Almeida, Hyggo, Perkusich, Angelo
• Format: Article
• Language: English
• Subjects: Case study; Coding; Data analysis; Data collection; data-driven decision-making; Decision analysis; Decision making; Encoding; Interviews; managerial decision-making; Mathematical analysis; Organizational aspects; Organizations; Project management; Software; software analytics; Software development; Software engineering; use cases
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3294823

Organizations have used software engineering data to support decision-making by applying data-driven approaches such as software analytics. However, adopting analytics tools depends on the information they provide and the real needs of practitioners. Significant research has addressed the needs of developers, whereas the needs of managers are not well understood. Moreover, few studies have focused on the practitioners' view of data-driven decision-making. From a managerial viewpoint, this case study provides an in-depth analysis of the information needs and the perceptions of data-driven decision-making of practitioners from one software development organization. We interviewed personnel in leadership positions and used coding procedures (open and selective coding) to analyze the collected data. We identified 19 software analytics use cases and mapped them to the software life cycle processes from ISO/IEC/IEEE 12207:2017, of which organizational project-enabling and technical management processes were the most highlighted by the interviewees. We also provided a set of indicators to meet the identified use cases and shed light on critical aspects of the organization's analytics scenario. Furthermore, we identified project-related, human-related, and context-specific factors that affect managerial decision-making and organizational aspects that influence the adoption of software analytics initiatives. Although our results are particularly relevant to organizations similar to the one described herein, they aim to serve as input for implementing new analytics solutions by practitioners and researchers in general and contribute to the body of knowledge on the topic from a practitioner's perspective, helping organizations in their attempts to adopt data-driven approaches.