Are You a Real Software Engineer? Best Practices in Online Recruitment for Software Engineering Studies
Adam Alami, Mansooreh Zahedi, Neil Ernst
TL;DR
The paper addresses the difficulty of recruiting qualified software engineers for online SE research and proposes a six-step best-practice framework to enhance data quality on Prolific. It leverages three SE studies to demonstrate iterative prescreening, task-oriented free-text screening, AI-detection, qualitative assessment, and staged compensation to verify participant authenticity and skill. The main contributions are concrete methodological guidelines for recruitment and screening that improve sample relevance and reliability, with discussion on limitations and transferability to other platforms. The work has practical implications for producing high-quality SE empirical data and informing future standards in online participant recruitment.
Abstract
Online research platforms, such as Prolific, offer rapid access to diverse participant pools but also pose unique challenges in participant qualification and skill verification. Previous studies reported mixed outcomes and challenges in leveraging online platforms for the recruitment of qualified software engineers. Drawing from our experience in conducting three different studies using Prolific, we propose best practices for recruiting and screening participants to enhance the quality and relevance of both qualitative and quantitative software engineering (SE) research samples. We propose refined best practices for recruitment in SE research on Prolific. (1) Iterative and controlled prescreening, enabling focused and manageable assessment of submissions (2) task-oriented and targeted questions that assess technical skills, knowledge of basic SE concepts, and professional engagement. (3) AI detection to verify the authenticity of free-text responses. (4) Qualitative and manual assessment of responses, ensuring authenticity and relevance in participant answers (5) Additional layers of prescreening are necessary when necessary to collect data relevant to the topic of the study. (6) Fair or generous compensation post-qualification to incentivize genuine participation. By sharing our experiences and lessons learned, we contribute to the development of effective and rigorous methods for SE empirical research. particularly the ongoing effort to establish guidelines to ensure reliable data collection. These practices have the potential to transferability to other participant recruitment platforms.