Background: Modern code review is expected to facilitate knowledge sharing: All relevant information, the collective expertise, and meta-information around the code change and its context become evident, transparent, and explicit in the corresponding code review discussion. The discussion participants can leverage this information in the following code reviews; the information diffuses through the communication network that emerges from code review. Traditional time-aggregated graphs fall short in rendering information diffusion as those models ignore the temporal order of the information exchange: Information can only be passed on if it is available in the first place.

Aim: This manuscript presents a novel model based on time-varying hypergraphs for rendering information diffusion that overcomes the inherent limitations of traditional, time-aggregated graph-based models. 

Method: In an in-silico experiment, we simulate an information diffusion within the internal code review at Microsoft and show the empirical impact of time on a key characteristic of information diffusion: the number of reachable participants. 

Results: Time-aggregation significantly overestimates the paths of information diffusion available in communication networks and, thus, is neither precise nor accurate for modelling and measuring the spread of information within communication networks that emerge from code review. 

Conclusion: Our model overcomes the inherent limitations of traditional, static or time-aggregated, graph-based communication models and sheds the first light on information diffusion through code review. We believe that our model can serve as a foundation for understanding, measuring, managing, and improving knowledge sharing in code review in particular and information diffusion in software engineering in general.

Background

Code review, the discussion around a code change among humans, forms a communication network that enables its participants to exchange and spread information. Although reported by qualitative studies, our understanding of the capability of code review as a communication network is still limited.

Objective

In this article, we report on a first step towards understanding and evaluating the capability of code review as a communication network by quantifying how fast and how far information can spread through code review: the upper bound of information diffusion in code review.

Method

In an in-silico experiment, we simulate an artificial information diffusion within large (Microsoft), mid-sized (Spotify), and small code review systems (Trivago) modelled as communication networks. We then measure the minimal topological and temporal distances between the participants to quantify how far and how fast information can spread in code review.

Results

An average code review participants in the small and mid-sized code review systems can spread information to between 72 % and 85 % of all code review participants within four weeks independently of network size and tooling; for the large code review systems, we found an absolute boundary of about 11 000 reachable participants. On average (median), information can spread between two participants in code review in less than five hops and less than five days.

Conclusion

We found evidence that the communication network emerging from code review scales well and spreads information fast and broadly, corroborating the findings of prior qualitative work. The study lays the foundation for understanding and improving code review as a communication network.

Background

As a core practice in software engineering, the nature of code review has been researched extensively: Prior exploratory studies theorized that code review, the discussion around a code change among humans, forms a communication network that enables its participants to exchange and spread information. Although prior exploratory studies lay an valuable foundation in understanding code review as communication network, the missing confirmatory counterpart leaves the theory's validity uncertain, limiting its credibility, practical applicability, and potential for further advancemcents.

Objective

This study aims to (1) formalize the theory of code review as a communication network explicit and (2) empirically test its validity by quantifying how widely and how quickly information can spread code review.

Method

We replicate an in silico experiment simulating information diffusion—the spread of information among participants—under best-case conditions across three open-source (Android, Visual Studio Code, React) and three closed-source code review systems (Microsoft, Spotify, Trivago) each modelled as a communication network. By measuring the number of reachable participants and the minimal topological and temporal distances, we quantify how widely and how quickly information can spread through code review.

Results

We find that code review networks can, under best-case conditions, support both wide and fast information diffusion, even in large-scale systems such as Microsoft’s internal code review platform. This confirms core assumptions of the theory of code review as a communication network. However, this capability is not uniformly present across all systems. Notably, we observe substantial differences between open-source and closed-source settings: open-source projects tend to achieve faster diffusion, while closed-source systems enable information to reach a broader share of participants.

Background

Code review, a core practice in software engineering, has been widely studied as a collaborative process, with prior work suggesting it functions as a communication network. Despite its popularity, this theory has not been formalized and remains untested, limiting its practical and theoretical significance.

Objective

This study aims to (1) formalize the theory of code review as a communication network explicit and (2) empirically test its validity by quantifying the extent of information diffusion---the spread of information---in code review across social, organizational, and software architectural boundaries.

Method

We conduct a large-scale empirical analysis of 220,733 code reviews by 2,246 developers at Spotify during 2019. We conceptualize information diffusion along three distinct boundaries: social (dissimilarity among review participants), organizational (involvement of developers across teams), and architectural (interconnections among the components under review).

Results

We find that over 99.6% of review pairs have completely distinct participant sets, indicating high diffusion across social boundaries. Approximately 18% of code reviews involve developers from multiple teams, evidencing nontrivial diffusion across organizational boundaries. Of the 5.82% of code reviews linked to others, 99.0% span distinct repositories, reflecting architectural diffusion.

Conclusion

The substantial diffusion of information across social, organizational, and architectural boundaries empirically supports the theory of code review as a communication network. These findings indicate that code review plays a role not only in quality assurance, but also in enabling communication and coordination in large-scale, distributed software projects. They further support its use as a measurable proxy for cross-border collaboration in the context of tax compliance, but also raise concerns about the impact of integrating LLMs on its communicative function.

The engineering of complex software systems is often the result of a highly collaborative effort. However, collaboration within a multinational enterprise has an overlooked legal implication when developers collaborate across national borders: It is taxable. In this article, we discuss the unsolved problem of taxing collaborative software engineering across borders.

Code review has long been a core practice in collaborative software engineering. In this research, we explore how practitioners reflect on code review today and what changes they anticipate in the near future. We then discuss the potential long-term risks of these anticipated changes for the evolution of code review and its role in collaborative software engineering.