Add to ORKGThe increase of computer processor speed and the ubiquitous availability of data coming from a diversity of sources (e.g., version control systems, software developers forums, operating system logs, etc.) have boosted the interest in applying machine learning to software engineering. Accordingly, the research literature on this topic has increased rapidly. This paper provides a comprehensive overview of that literature for the last five years. To do so, it examines 1,312 records gathered from Elsevier Scopus, identifying (i) the most productive authors and their collaboration networks, (ii) the countries and institutions that are leading research, (iii) the journals that are publishing the most papers, and (iv) the most important research...
The advancements in machine learning techniques have encouraged researchers to apply these technique...
Artificial intelligence enabled systems have been an inevitable part of everyday life. However, effi...
This article presents our work in progress in supporting automated machine learning in the model-dri...
The increase of computer processor speed and the ubiquitous availability of data coming from a diver...
Dataset of the research paper: Machine Learning for Software Engineering: A Tertiary Study Machine ...
Machine Learning (ML) is the discipline that studies methods for automatically inferring models from...
The application of machine learning solutions in software engineering tools and processes can bring ...
Artificial intelligence enabled systems have been an inevitable part of everyday life. However, effi...
[[abstract]]Machine learning is the study of building computer programs that improve their performan...
This paper carries out a bibliometric analysis to detect (i) what is the most influential research o...
Software engineering is one of the most utilizable research areas for data mining. Developers have a...
[[abstract]]Machine learning deals with the issue of how to build programs that improve their perfor...
The purpose of the software manufacturing industry is to produce high-quality applications that meet...
In the last couple of years we have witnessed an enormous increase of machine learning (ML) applicat...
The increasing reliance on applications with machine learning (ML) components calls for mature engin...
The advancements in machine learning techniques have encouraged researchers to apply these technique...
Artificial intelligence enabled systems have been an inevitable part of everyday life. However, effi...
This article presents our work in progress in supporting automated machine learning in the model-dri...
The increase of computer processor speed and the ubiquitous availability of data coming from a diver...
Dataset of the research paper: Machine Learning for Software Engineering: A Tertiary Study Machine ...
Machine Learning (ML) is the discipline that studies methods for automatically inferring models from...
The application of machine learning solutions in software engineering tools and processes can bring ...
Artificial intelligence enabled systems have been an inevitable part of everyday life. However, effi...
[[abstract]]Machine learning is the study of building computer programs that improve their performan...
This paper carries out a bibliometric analysis to detect (i) what is the most influential research o...
Software engineering is one of the most utilizable research areas for data mining. Developers have a...
[[abstract]]Machine learning deals with the issue of how to build programs that improve their perfor...
The purpose of the software manufacturing industry is to produce high-quality applications that meet...
In the last couple of years we have witnessed an enormous increase of machine learning (ML) applicat...
The increasing reliance on applications with machine learning (ML) components calls for mature engin...
The advancements in machine learning techniques have encouraged researchers to apply these technique...
Artificial intelligence enabled systems have been an inevitable part of everyday life. However, effi...
This article presents our work in progress in supporting automated machine learning in the model-dri...