What is he best known for?
The fields of study he is best known for:
- Programming language
- Operating system
- Software
Software maintenance, Software, Programming language, Software evolution and Code are his primary areas of study.
He combines subjects such as Software engineering and Code refactoring with his study of Software maintenance.
His studies in Software integrate themes in fields like Software deployment and Knowledge management.
His work on Set, Exception handling and Legacy system as part of general Programming language study is frequently linked to Reuse, bridging the gap between disciplines.
Miryung Kim works mostly in the field of Software evolution, limiting it down to concerns involving Data mining and, occasionally, Software versioning, Regression testing and Block.
The various areas that Miryung Kim examines in his Code study include Java and Information retrieval.
His most cited work include:
- An empirical study of code clone genealogies (467 citations)
- Managing technical debt in software-reliant systems (256 citations)
- An ethnographic study of copy and paste programming practices in OOPL (231 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Software, Programming language, Software engineering, Code and Code refactoring.
His work carried out in the field of Software brings together such families of science as Java and Data science.
His work on Scripting language and Semantics as part of general Programming language research is frequently linked to Reuse and Transplantation, thereby connecting diverse disciplines of science.
The Software engineering study combines topics in areas such as Software development and Software bloat.
His study in the fields of Clone under the domain of Code overlaps with other disciplines such as Process.
His Code refactoring research is multidisciplinary, relying on both Software maintenance, Logic programming, Software bug, Correctness and Set.
He most often published in these fields:
- Software (38.32%)
- Programming language (28.04%)
- Software engineering (26.17%)
What were the highlights of his more recent work (between 2018-2021)?
- Big data (13.08%)
- Software (38.32%)
- Dataflow (5.61%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Big data, Software, Dataflow, Software engineering and Code coverage.
His studies deal with areas such as Debugging, Computer engineering and Data analysis as well as Big data.
His Software study focuses on Software quality in particular.
The study incorporates disciplines such as Test data, SQL and Data mining in addition to Dataflow.
His Software engineering research integrates issues from Heuristics, Commit, Software bloat and Code smell.
His Code coverage research incorporates themes from Class, Test suite and Fuzz testing.
Between 2018 and 2021, his most popular works were:
- An Empirical Study of Common Challenges in Developing Deep Learning Applications (30 citations)
- Analyzing and supporting adaptation of online code examples (16 citations)
- Active inductive logic programming for code search (10 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Operating system
- Software
The scientist’s investigation covers issues in Empirical research, Construct, Code coverage, Artificial intelligence and Deep learning.
He integrates many fields, such as Empirical research and engineering, in his works.
His Construct study combines topics from a wide range of disciplines, such as Code, Information retrieval, Adaptation and Taxonomy.
His Code coverage study integrates concerns from other disciplines, such as SQL, Distributed computing, White-box testing, Class and Machine learning.
His Artificial intelligence study combines topics in areas such as Test suite and Software development.
His work deals with themes such as Field, Intelligent decision support system, Software quality, Data science and Profiling, which intersect with Deep learning.
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