Table Of Links Table Of Links ABSTRACT ABSTRACT 1 INTRODUCTION 1 INTRODUCTION 1 INTRODUCTION 2 BACKGROUND 2 BACKGROUND 2 BACKGROUND 2.1 Code Review As Communication Network 2.2 Code Review Networks 2.3 Measuring Information Diffusion in Code Review 3 RESEARCH DESIGN 3 RESEARCH DESIGN 3 RESEARCH DESIGN 3.1 Hypotheses 3.2 Measurement model 3.3 Measuring system 4 LIMITATIONS 4 LIMITATIONS 4 LIMITATIONS ACKNOWLEDGMENTS AND REFERENCES ACKNOWLEDGMENTS AND REFERENCES ACKNOWLEDGMENTS AND REFERENCES 4 LIMITATIONS 4 LIMITATIONS In general, the chain of evidence of our study depends on two main factors: (1) the measurement model, measuring system, and actual measurement, and (2) the thoroughness of our discussion for qualitatively rejecting the hypotheses and, thereby, falsifying the theory of code review as communication network. Although we will not be able to provide the complete raw data and only a prototypical extraction pipeline for Backstage, we believe that our thorough description of our measurement model, measuring system, and the actual measurement at Spotify provides a solid foundation for this line of research. Our replication package will contain the necessary yet anonymized data to reproduce and replicate our study beyond the context of Spotify. However, as for every data-driven study, missing, incomplete, faulty, or unreliable data may significantly affect the validity of our study. To mitigate those risks, we conducted a pilot study in October 2023. Although we have not encountered such threats to validity, we cannot exclude data-related limitations. Therefore, this section will also cover the limitations that come from excluding or missing data once our data collection is completed. However, we believe the two most critical limitations of our study lie in the nature of a qualitative falsification of theories. Although traditional statistical hypothesis tests also have their limitations and, ultimately, also represent an implicit and qualitative discussion, we believe that a discussion remains more prone to bias, most importantly because there are no clear criteria to reject the hypotheses upfront. Such clear rejection and falsification criteria are not possible and meaningful upfront for this research; all thresholds, values, or estimates would be arbitrary. However, we believe that a comprehensive discussion makes a potential bias explicit and allows other researchers to conclude differently. Additionally, we will publish our measurement system and all intermediate anonymized data to enable other researchers to replicate our work. Second, even if our data and a thorough discussion suggest falsifying our theory by rejecting one of the hypotheses, our modelling approach may not capture the (relevant) information diffusion in code review. Although we have strong indications that the explicit referencing of code reviews is an active and explicit information diffusion triggered by human assessment, we are not aware of empirical evidence that supports our assumption. Although already discussed in Section 3, we emphasize again that the findings of the extent of information diffusion will not be generalizable. We do not believe that this is a major limitation of our research design since our argumentation is based on contradiction (reductio ad absurdum). This section will also include a detailed discussion of limitations that originate in incomplete or missing data when they become visible after the data collection and analysis. 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Authors: Michael Dorner Daniel Mendez Ehsan Zabardast Nicole Valdez Marcin Floryan Authors: Authors: Michael Dorner Daniel Mendez Ehsan Zabardast Nicole Valdez Marcin Floryan Michael Dorner Michael Dorner Daniel Mendez Daniel Mendez Ehsan Zabardast Ehsan Zabardast Nicole Valdez Nicole Valdez Marcin Floryan Marcin Floryan This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv n arxiv
