The dataset contains code quality information of more than 86 thousand GitHub repositories containing more than 1.1 billion lines of code mainly written in C# and Java. The code quality information contains detected 7 kinds of architecture smells, 19 kinds of design smells, and 11 kinds of implementation smells, and 27 commonly used code quality metrics computed at project, package, class, and method levels
Code smells are symptoms of poor design and implementation choices, which might hinder comprehension...
Code quality remains an abstract concept that fails to get traction at the business level. Consequen...
Pitfalls in software development process can be prevented by learning from other people's mistakes. ...
The dataset contains code quality information of more than 86 thousand GitHub repositories containin...
The dataset contains 10 different repositories with its quality, source code metrics and code smells...
Source code for QScored Agent. Supplimentary package for "QScored: A Large Dataset of Code Smells an...
Research software has opened up new pathways of discovery in many and diverse disciplines. The resea...
Research software has opened up new pathways of discovery in many and diverse disciplines. The resea...
This code smells dataset collected from Git history of top-100 Java projects. It contains 5912 sampl...
The dataset contains quality, source code metrics information of 60 versions under 10 different repo...
<p>This dataset includes classes with code smells, acquired from Qualitas Corpus (QC).<br> Folder 'a...
Background: Code smells are indicators of quality problems that make a software hard to maintain and...
This dataset contains code smells (implementation, design, and architecture smells) mined from 3073 ...
The MLCQ data set with nearly 15000 code samples was created by software developers with professiona...
Different challenges arise while detecting deficient software source code. Usually a large number of...
Code smells are symptoms of poor design and implementation choices, which might hinder comprehension...
Code quality remains an abstract concept that fails to get traction at the business level. Consequen...
Pitfalls in software development process can be prevented by learning from other people's mistakes. ...
The dataset contains code quality information of more than 86 thousand GitHub repositories containin...
The dataset contains 10 different repositories with its quality, source code metrics and code smells...
Source code for QScored Agent. Supplimentary package for "QScored: A Large Dataset of Code Smells an...
Research software has opened up new pathways of discovery in many and diverse disciplines. The resea...
Research software has opened up new pathways of discovery in many and diverse disciplines. The resea...
This code smells dataset collected from Git history of top-100 Java projects. It contains 5912 sampl...
The dataset contains quality, source code metrics information of 60 versions under 10 different repo...
<p>This dataset includes classes with code smells, acquired from Qualitas Corpus (QC).<br> Folder 'a...
Background: Code smells are indicators of quality problems that make a software hard to maintain and...
This dataset contains code smells (implementation, design, and architecture smells) mined from 3073 ...
The MLCQ data set with nearly 15000 code samples was created by software developers with professiona...
Different challenges arise while detecting deficient software source code. Usually a large number of...
Code smells are symptoms of poor design and implementation choices, which might hinder comprehension...
Code quality remains an abstract concept that fails to get traction at the business level. Consequen...
Pitfalls in software development process can be prevented by learning from other people's mistakes. ...