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Thomas Bock

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Review Open access Aug 2026

How developer coreness influences the patch-review process: A mixed-method study

The code integration process is critical for any distributed, large-scale open-source software (OSS) project. It serves as an implicit or explicit quality control gate and is inherently of a socio-technical nature in that the bare technical act of merging new code contributions is preceded by (oftentimes engaged) discussions and reviews. Given the reasonable and widely accepted assumption that professional experience and seniority lead to higher social credit in communities, more experienced developers are expected to get favored in this process, manifesting in higher probabilities of receiving feedback on contributions, or getting contributions accepted. We conjecture that exceptions to this pattern may indicate procedural issues and examine this hypothesis through a mixed-method study. To this end, we use developer coreness, a continuous proxy measure of experience that measures how important and connected a developer is within a project. We then study code integration processes of 16 popular OSS projects, employing a new methodology to measure the impact of developer coreness on these processes. This allows us to identify process-deviant projects, which we investigate qualitatively to determine whether unexpected observations indicate underlying procedural issues. Our findings show that developers with higher coreness values have a higher probability of getting code contributions accepted and, in many cases, of receiving feedback. Notably, projects identified as process-deviant often exhibit signs of procedural deficiencies, highlighting the practical utility of our methodological framework.

Christian Hechtl, Thomas Bock, Ralf Ramsauer et al. · 0 citations
Preprint Aug 2026

The Ground Is Shifting: A Reflection on the Foundations of Software Measurement

For most of the past six decades, software measurement relied on labor-intensive manual collection of proprietary data, which hampered progress. The shift to repurposing traces from version control and related tools dramatically expanded data availability$\unicode{x2014}$especially with the rise of open-source software$\unicode{x2014}$but hinged on an often unstated assumption: that these tools are used by professional developers to build genuine software systems. However, as trace-generating tools, data types and scale, and empirical methods have all evolved, it has become clear that changes in data generation and analytical approaches affect many prior findings about software development, maintenance, and evolution. With AI agents now actively using these same tools, the resulting traces frequently violate the original assumption of human origin. To preserve the relevance of software measurement research, immediate action is needed: We must detect when foundational assumptions are violated in contemporary data and develop new methodologies that remain valid under changed circumstances. To this end, we propose a systematic AI-assisted replication program that revisits key findings using modern techniques, aiming for methods that yield consistent results on current data to keep software measurement meaningful.

Thomas Bock, Audris Mockus, Bogdan Vasilescu · 0 citations