What is he best known for?
The fields of study he is best known for:
- Software
- Operating system
- Programming language
His primary areas of study are Source code, Software, Information retrieval, Data mining and Software system.
Denys Poshyvanyk has included themes like Software bug, World Wide Web and Feature in his Source code study.
His research integrates issues of Java and Artificial intelligence in his study of Software.
His studies in Information retrieval integrate themes in fields like Theoretical computer science, Traceability and Programmer.
His Data mining research includes themes of Coupling, Probabilistic logic, Latent semantic indexing, Search engine indexing and Ranking.
The various areas that he examines in his Software system study include Topic model and Identifier.
His most cited work include:
- Feature location in source code: a taxonomy and survey (447 citations)
- Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval (378 citations)
- Deep learning code fragments for code clone detection (282 citations)
What are the main themes of his work throughout his whole career to date?
Denys Poshyvanyk spends much of his time researching Software, Source code, Software engineering, Android and Information retrieval.
His Software quality study, which is part of a larger body of work in Software, is frequently linked to Empirical research, bridging the gap between disciplines.
The study incorporates disciplines such as Software system, Software maintenance, Data mining and Artificial intelligence in addition to Source code.
His work carried out in the field of Data mining brings together such families of science as Program comprehension, Machine learning, Feature, KPI-driven code analysis and Identifier.
His Software engineering research is multidisciplinary, relying on both Software evolution, Software development, Software metric, Software construction and Test case.
His work deals with themes such as Graphical user interface, Human–computer interaction and World Wide Web, Mobile device, Mobile apps, which intersect with Android.
He most often published in these fields:
- Software (41.63%)
- Source code (41.20%)
- Software engineering (29.61%)
What were the highlights of his more recent work (between 2017-2021)?
- Android (22.32%)
- Software (41.63%)
- Artificial intelligence (16.31%)
In recent papers he was focusing on the following fields of study:
Android, Software, Artificial intelligence, Software engineering and Source code are his primary areas of study.
His studies deal with areas such as Graphical user interface, Human–computer interaction, Static program analysis and World Wide Web, Mobile device as well as Android.
As a member of one scientific family, he mostly works in the field of Software, focusing on Component and, on occasion, Data mining and Regression testing.
His biological study spans a wide range of topics, including Natural language processing, Machine learning and Code.
Denys Poshyvanyk has researched Software engineering in several fields, including Test case and Mobile apps.
In his study, which falls under the umbrella issue of Source code, Commit is strongly linked to Code refactoring.
Between 2017 and 2021, his most popular works were:
- SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair (64 citations)
- On learning meaningful code changes via neural machine translation (50 citations)
- Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities (48 citations)
In his most recent research, the most cited papers focused on:
- Software
- Operating system
- Programming language
Denys Poshyvanyk focuses on Artificial intelligence, Android, Software, Source code and Code.
His research in Artificial intelligence intersects with topics in Java, Software bug, Machine learning and Natural language processing.
His Android study combines topics in areas such as Graphical user interface, Program comprehension, Human–computer interaction and Internet privacy.
With his scientific publications, his incorporates both Software and Empirical research.
His Source code research is under the purview of Programming language.
His Code research includes elements of Software development and Machine translation.
This overview was generated by a machine learning system which analysed the scientist’s
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