Checklist and data extracted from publications analyzed for "Code Smells Detection Using Artificial Intelligence Techniques: A Business-Driven Systematic Review" paper Details regarding this data set can be found in the paper available under: https://doi.org/10.1007/978-3-030-77916-0_1
The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife pre...
The MLCQ data set with nearly 15000 code samples was created by software developers with professiona...
A.S.C. and G.d.F.C. together searched for eligible papers from the publication databases and read th...
Checklist and data extracted from publications analyzed for "Code Smells Detection Using Artificial ...
Checklist and data extracted from publications analyzed for "How far are we from reproducible resear...
Full reproduction package for the research published in "Detecting code smells using industry-releva...
Code smells are symptoms of design shortcomings in source code. There are various tools and approach...
High data quality is fundamental for today's AI-based systems. However, although data quality has be...
DESCRIPTION This dataset contains the structure, collected data and descriptive statistics of respo...
As a type of anti-pattern, test smells are defined as poorly designed tests and their presence may n...
This repository contains the data and results from the paper "Code Smells Detection via Code Review:...
The popularity of machine learning has wildly expanded in recent years. Machine learning techniques ...
\u3cp\u3eCode smells are symptoms of poor design and implementation choices weighing heavily on the ...
Code smells indicate the presence of quality problems that make the software hard to maintain and ev...
Code smells are symptoms of poor design and implementation choices, which might hinder comprehension...
The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife pre...
The MLCQ data set with nearly 15000 code samples was created by software developers with professiona...
A.S.C. and G.d.F.C. together searched for eligible papers from the publication databases and read th...
Checklist and data extracted from publications analyzed for "Code Smells Detection Using Artificial ...
Checklist and data extracted from publications analyzed for "How far are we from reproducible resear...
Full reproduction package for the research published in "Detecting code smells using industry-releva...
Code smells are symptoms of design shortcomings in source code. There are various tools and approach...
High data quality is fundamental for today's AI-based systems. However, although data quality has be...
DESCRIPTION This dataset contains the structure, collected data and descriptive statistics of respo...
As a type of anti-pattern, test smells are defined as poorly designed tests and their presence may n...
This repository contains the data and results from the paper "Code Smells Detection via Code Review:...
The popularity of machine learning has wildly expanded in recent years. Machine learning techniques ...
\u3cp\u3eCode smells are symptoms of poor design and implementation choices weighing heavily on the ...
Code smells indicate the presence of quality problems that make the software hard to maintain and ev...
Code smells are symptoms of poor design and implementation choices, which might hinder comprehension...
The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife pre...
The MLCQ data set with nearly 15000 code samples was created by software developers with professiona...
A.S.C. and G.d.F.C. together searched for eligible papers from the publication databases and read th...
DOI
Add to ORKG