SEJun 7, 2021

Adopting Softer Approaches in the Study of Repository Data: A Comparative Analysis

arXiv:2106.03309v19 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the need for deeper contextual insights into software teams' processes, offering methodological advice for researchers, but it is incremental as it builds on existing qualitative approaches.

The study tackled the problem of understanding software development team dynamics by comparing psycholinguistics and directed content analysis on repository data, revealing significant differences in team work based on tasks and roles.

Context: Given the acknowledged need to understand the people processes enacted during software development, software repositories and mailing lists have become a focus for many studies. However, researchers have tended to use mostly mathematical and frequency-based techniques to examine the software artifacts contained within them. Objective: There is growing recognition that these approaches uncover only a partial picture of what happens during software projects, and deeper contextual approaches may provide further understanding of the intricate nature of software teams' dynamics. We demonstrate the relevance and utility of such approaches in this study. Method: We use psycholinguistics and directed content analysis (CA) to study the way project tasks drive teams' attitudes and knowledge sharing. We compare the outcomes of these two approaches and offer methodological advice for researchers using similar forms of repository data. Results: Our analysis reveals significant differences in the way teams work given their portfolio of tasks and the distribution of roles. Conclusion: We overcome the limitations associated with employing purely quantitative approaches, while avoiding the time-intensive and potentially invasive nature of field work required in full case studies.

Foundations

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