A rule-based decision model to support technical debt decisions: A multiple case study of web and mobile app startups

Context: Software startups are immature software organizations that focus on the development of a single software product or service. This organizational context accumulates a lot of technical debt to cope with constraints such as limited resources and product-market fit uncertainty. While some research has explored technical debt in startups, there is no study that investigates how software startups should make technical debt decisions throughout the startup evolution stages. Objective: The objective of this study is to understand how technical debt decisions are made, and how such decisions should have been made in hindsight. Method: We conducted a multiple embedded case study to investigate technical debt decisions in five web/mobile app startups. For each case, we interviewed the case founder and developer (a total of 17 participants across cases). In addition, we collected some public documents about the five startups. The data were analyzed using qualitative data analysis techniques. Results: We developed a rule-based decision model that summarizes the logic to effectively make technical debt decisions throughout the startup evolution stages. In addition, we evaluated the model by conducting follow-up interviews with three participants. Conclusion: The study provides a decision model that reflects actual practice, and is designed to help software teams in startups when making technical debt decisions throughout the startup evolution stages.