Modelling Hypotheses in Continuous Experimentation: A Template-Based Approach

Abstract

Context: In the pursuit of efficient methodologies for assessing user value and product success in the market, organizations are increasingly turning to strategic practices such as extensive market research and analysis. However, companies typically operate with a high degree of uncertainty and face the challenge of quickly validating their ideas and achieving market traction. As the software development lifecycle becomes more iterative and rapid, the need for efficient and effective continuous experimentation (CE) becomes paramount.

Objective: The primary objective of this research is to develop a structured and standardized hypothesis template model that can formalize assumptions into well-formed, testable hypotheses, yielding meaningful experimentation results, thereby automating and improving this critical component of the CE process.

Method: To achieve the research objectives, a mixed-methods approach was employed, combining a systematic literature review (SLR) with a multiple-case study strategy. The SLR provided a foundational understanding of existing hypothesis types and structures, while the multiple-case study offered practical insights into current industry practices and challenges associated with hypothesis formulation.

Result: The results demonstrate that the proposed hypothesis template model effectively captures the essential components of the hypothesis, providing a comprehensive template model for modelling hypotheses. The model was validated through feedback from industry experts, highlighting its strengths in promoting consistency and reducing human error.

Conclusion: In conclusion, the research presents a robust hypothesis model that can significantly enhance the hypothesis-driven experimentation process in software development. By addressing common challenges and offering a clear framework, the model facilitates more effective and efficient CE. Future research could focus on improving the model to better address hypotheses related to problem space, exploring how artificial intelligence (AI) could assist in generating prompts and templates for hypothesis formulation and how the hypothesis template model could be implemented in machine-readable format to support CE automation.