Search for: All records

Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality it is desirable to identify bots in order to separate their actions from actions of individuals. Aim: Find an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns. Method and Result: We propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the ommits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of whom have more than 1000 commits) and 13,762,430 commits they created. 

FLOSS ecosystem as a whole is a critical component of world’s computing infrastructure, yet not well understood. In order to understand it well, we need to measure it first. We, therefore, aim to provide a framework for measuring key aspects of the entire FLOSS ecosystem. We first consider the FLOSS ecosystem through lens of a supply chain. The concept of supply chain is the existence of series of interconnected parties/affiliates each contributing unique elements and expertise so as to ensure a final solution is accessible to all interested parties. This perspective has been extremely successful in helping allowing companies to cope with multifaceted risks caused by the distributed decision-making in their supply chains, especially as they have become more global. Software ecosystems, similarly, represent distributed decisions in supply chains of code and author contributions, suggesting that relationships among projects, developers, and source code have to be measured. We then describe a massive measurement infrastructure involving discovery, extraction, cleaning, correction, and augmentation of publicly available open-source data from version control systems and other sources. We then illustrate how the key relationships among the nodes representing developers, projects, changes, and files can be accurately measured, how to handle absence of measures for user base in version control data, and, finally, illustrate how such measurement infrastructure can be used to increase knowledge resilience in FLOSS.