QScored: A Large Dataset of Code Smells and Quality Metrics

Abstract

Code quality aspects such as code smells and code quality metrics are widely used in exploratory and empirical software engineering research. In such studies, researchers spend a substantial amount of time and effort to not only select the appropriate subject systems but also to analyze them to collect the required code quality information. In this paper, we present QScored dataset; the dataset contains code quality information of more than 86 thousand C# and Java GitHub repositories containing more than 1.1 billion lines of code. The code quality information contains seven kinds of detected architecture smells, 20 kinds of design smells, eleven kinds of implementation smells, and 27 commonly used code quality metrics computed at project, package, class, and method levels. Availability of the dataset will facilitate empirical studies involving code quality aspects by making the information readily available for a large number of active GitHub repositories.