What is he best known for?
The fields of study he is best known for:
- Software
- Artificial intelligence
- Software engineering
Mika V. Mäntylä spends much of his time researching Software engineering, Software, Data science, Systematic review and Software development.
The Software engineering study combines topics in areas such as Empirical research and Software quality, Code smell.
His Code smell study deals with Artificial intelligence intersecting with Machine learning.
His Software research focuses on Maintainability and how it connects with Software metric.
His Data science research incorporates themes from Topic model, Sentiment analysis, Subjectivity analysis and Computational linguistics.
His work on Software development process as part of general Software development study is frequently linked to Research program, therefore connecting diverse disciplines of science.
His most cited work include:
- The evolution of sentiment analysis—A review of research topics, venues, and top cited papers (166 citations)
- A taxonomy and an initial empirical study of bad smells in code (152 citations)
- Comparing and experimenting machine learning techniques for code smell detection (138 citations)
What are the main themes of his work throughout his whole career to date?
Mika V. Mäntylä mainly investigates Software, Software engineering, Data science, Software development and Test.
His Software study combines topics from a wide range of disciplines, such as Sentiment analysis, Root cause analysis and Source code.
His biological study spans a wide range of topics, including Quality, Empirical research and Software quality, Code smell.
His Data science research integrates issues from Topic model and Software repository.
His research investigates the link between Software development and topics such as Agile software development that cross with problems in Software development process.
His Test research is multidisciplinary, incorporating elements of Exploratory testing, Manual testing and Knowledge management.
He most often published in these fields:
- Software (41.22%)
- Software engineering (38.17%)
- Data science (18.32%)
What were the highlights of his more recent work (between 2017-2021)?
- Software (41.22%)
- Software engineering (38.17%)
- Source code (10.69%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Software, Software engineering, Source code, Software development and Data science.
His studies in Software integrate themes in fields like Sentiment analysis, World Wide Web and Engineering management.
The concepts of his Software engineering study are interwoven with issues in Quality, Pipeline and Natural language.
His Quality research is multidisciplinary, relying on both Software quality and Process.
While the research belongs to areas of Software development, Mika V. Mäntylä spends his time largely on the problem of Agile software development, intersecting his research to questions surrounding Lean manufacturing, Software development process and Software deployment.
The various areas that Mika V. Mäntylä examines in his Data science study include Set, Unit of analysis and Software repository.
Between 2017 and 2021, his most popular works were:
- The evolution of sentiment analysis—A review of research topics, venues, and top cited papers (166 citations)
- Guidelines for including grey literature and conducting multivocal literature reviews in software engineering (128 citations)
- Test prioritization in continuous integration environments (16 citations)
In his most recent research, the most cited papers focused on:
- Software
- Artificial intelligence
- Software engineering
Mika V. Mäntylä mostly deals with Software engineering, Software, Software development, Empirical research and Quality.
Mika V. Mäntylä undertakes interdisciplinary study in the fields of Software engineering and Grey literature through his works.
Mika V. Mäntylä regularly ties together related areas like Sentiment analysis in his Software studies.
His study in Software development is interdisciplinary in nature, drawing from both Agile software development and DevOps.
His Empirical research research includes themes of Data science and Software repository.
His work on Quality assurance as part of general Quality study is frequently linked to Systematic review, bridging the gap between disciplines.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
