Modern code reviews improve the quality of software products. Although modern code reviews rely heavily on human interactions, little is known regarding whether they are performed fairly. Fairness plays a role in any process where decisions that affect others are made. When a system is perceived to be unfair, it affects negatively the productivity and motivation of its participants. In this paper, using fairness theory we create a framework that describes how fairness affects modern code reviews. To demonstrate its applicability, and the importance of fairness in code reviews, we conducted an empirical study that asked developers of a large industrial open source ecosystem (OpenStack) what their perceptions are regarding fairness in their code reviewing process. Our study shows that, in general, the code review process in OpenStack is perceived as fair; however, a significant portion of respondents perceive it as unfair. We also show that the variability in the way they prioritize code reviews signals a lack of consistency and the existence of bias (potentially increasing the perception of unfairness). The contributions of this paper are: (1) we propose a framework---based on fairness theory---for studying and managing social behaviour in modern code reviews, (2) we provide support for the framework through the results of a case study on a large industrial-backed open source project, (3) we present evidence that fairness is an issue in the code review process of a large open source ecosystem, and, (4) we present a set of guidelines for practitioners to address unfairness in modern code reviews.

In this paper, we present a collection of Modern Code Review data for five open source projects. The data showcases mined data from both an integrated peer review system and source code repositories. We present an easy-to-use and richer data structure to retrieve the (a) People, (b) Process, and (c) Product aspects of the peer review. This paper presents the extraction methodology, the dataset structure, and a collection of database dumps.

In recent times, gamification has become a popular technique to aid online communities stimulate active member participation. Gamification promotes a reward-driven approach, usually measured by response-time. Possible concerns of gamification could a trade-off between speedy over quality responses. Conversely, bias toward easier question selection for maximum reward may exist. In this study, we analyze the distribution gamification-influenced tendencies on the Q&A Stack Overflow online community. In addition, we define some gamification-influenced metrics related to response time to a question post. We carried experiments of a four-month period analyzing 101,291 members posts. Over this period, we determined a Rapid Response time of 327 seconds (5.45 minutes). Key findings suggest that around 92% of SO members have fewer rapid responses that non-rapid responses. Accepted answers have no clear relationship with rapid responses. However, we did find that rapid responses significantly contain tags that did not follow their usual tagging tendencies.

ReDA (http://reda.naist.jp/) is a web-based visualization tool for analyzing Modern Code Review (MCR) datasets for large Open Source Software (OSS) projects. MCR is a commonly practiced and lightweight inspection of source code using a support tool such as Gerrit system. Recently, mining code review history of such systems has received attention as a potentially effective method of ensuring software quality. However, due to increasing size and complexity of softwares being developed, these datasets are becoming unmanageable. ReDA aims to assist researchers of mining code review data by enabling better understand of dataset context and identifying abnormalities. Through real-time data interaction, users can quickly gain insight into the data and hone in on interesting areas to investigate. A video highlighting the main features can be found at: http://youtu.be/ fEoTRRas0U.

Software peer review (aka. code review) is regarded as one of the most important approaches to keep software quality and productivity. Due to the distributed collaborations and communication nature of Open Source Software (OSS), OSS review differs from traditional industry review. Unlike other related works, this study investigated OSS peer review pro- cesses from social perspective by using social network anal- ysis (SNA). We analyzed the review history from three typi- cal OSS projects. The results provide hints on relationships among the OSS reviewers which can help to understand how developers work and communicate with each other.

Due to the distributed collaborations and the volunteering nature of Open Source Software (OSS), OSS peer review processes differs from traditional approaches. Despite the latest research efforts to understand OSS peer review processes, very little is known. Unlike related work, this study investigates OSS peer review processes from a different perspective. We investigate the importance of OSS peer review contributor roles and their review activities by using social network analysis (SNA), proposed as PeRSoN (Peer Review Social Network). As a case study, we extracted and analyzed the review process of Android Open Source Project (AOSP). To the best of our knowledge, this is the first research constructing social networks from mining a peer review repository. Our preliminary results provided hints on relationships among the OSS peer review contributor roles, their activities, and the network structure. The results raised issues that will be used to refine our approach in the future.