Towards a Methodology for Participant Selection in Software Engineering Experiments. A Vision of the Future

TL;DR

This paper reviews current participant selection practices in Software Engineering experiments, identifies key threats to validity, and proposes a new methodology to improve sample representativeness and generalizability.

Contribution

It introduces a comprehensive methodology for participant selection in SE experiments, addressing current limitations and providing a roadmap for empirical validation.

Findings

Current practices often limit generalizability due to convenience sampling.

The proposed methodology defines desired population characteristics and sources for sampling.

A roadmap for empirical validation of the selection approach is outlined.

Abstract

Background. Software Engineering (SE) researchers extensively perform experiments with human subjects. Well-defined samples are required to ensure external validity. Samples are selected \textit{purposely} or by \textit{convenience}, limiting the generalizability of results. Objective. We aim to depict the current status of participants selection in empirical SE, identifying the main threats and how they are mitigated. We draft a robust approach to participants' selection. Method. We reviewed existing participants' selection guidelines in SE, and performed a preliminary literature review to find out how participants' selection is conducted in SE in practice. % and 3) we summarized the main issues identified. Results. We outline a new selection methodology, by 1) defining the characteristics of the desired population, 2) locating possible sources of sampling available for researchers, and 3) identifying and reducing the "distance" between the selected sample and its corresponding population. Conclusion. We propose a roadmap to develop and empirically validate the selection methodology.