What is he best known for?
The fields of study he is best known for:
- Programming language
- Software
- Operating system
Alexander Serebrenik mainly investigates Software, Software development, World Wide Web, Data mining and Knowledge management.
His work deals with themes such as Sentiment analysis, Process mining, Soft skills and Software engineering, which intersect with Software.
His work in Software development addresses issues such as Social media, which are connected to fields such as Development team and Knowledge-based systems.
His World Wide Web research is multidisciplinary, incorporating elements of Scalability, Open source development, Open source, Social coding and Popularity.
His studies deal with areas such as Software maintenance, Software metric, Anger, Collaborative software development and Collaborative software as well as Data mining.
The concepts of his Knowledge management study are interwoven with issues in Regression analysis, Open source software and Software development process.
His most cited work include:
- Process Discovery using Integer Linear Programming (263 citations)
- StackOverflow and GitHub: Associations between Software Development and Crowdsourced Knowledge (177 citations)
- Gender and Tenure Diversity in GitHub Teams (163 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Software engineering, Programming language, Software, Software development and Theoretical computer science.
His Software engineering study deals with Software quality intersecting with Quality.
The Software study combines topics in areas such as Sentiment analysis, Maintainability and Data science.
His study in Software development is interdisciplinary in nature, drawing from both World Wide Web and Knowledge management.
He combines subjects such as Algorithm, Correctness and Petri net with his study of Theoretical computer science.
His Software evolution study, which is part of a larger body of work in Software system, is frequently linked to Automotive industry, bridging the gap between disciplines.
He most often published in these fields:
- Software engineering (24.04%)
- Programming language (20.56%)
- Software (18.82%)
What were the highlights of his more recent work (between 2018-2021)?
- Software (18.82%)
- Software engineering (24.04%)
- Software development (16.03%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Software, Software engineering, Software development, Data science and Empirical research.
His Software study incorporates themes from Personal wellbeing and Source code.
His Source code study improves the overall literature in Programming language.
His research in the fields of Domain overlaps with other disciplines such as Interview study.
His Data science research is multidisciplinary, relying on both Sentiment analysis and Software quality.
His Java research includes elements of Actuarial science and Theoretical computer science.
Between 2018 and 2021, his most popular works were:
- Going farther together: the impact of social capital on sustained participation in open source (34 citations)
- Gender diversity and women in software teams: how do they affect community smells? (28 citations)
- Discovering community patterns in open-source: a systematic approach and its evaluation (28 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Software
- Operating system
Software, Software development, Data science, Software engineering and Open source are his primary areas of study.
The various areas that Alexander Serebrenik examines in his Software study include Group dynamic and Root cause.
His research integrates issues of Software bug and Internet privacy in his study of Software development.
His Data science study combines topics in areas such as Agile software development, Code smell and Software evolution.
His Software engineering research is multidisciplinary, incorporating perspectives in Sentiment analysis, Computational linguistics and Leverage.
His work carried out in the field of Open source brings together such families of science as Maintenance engineering and Process management.
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.
