What is she best known for?
The fields of study she is best known for:
- Operating system
- Programming language
- Software
Lin Tan mostly deals with Software, Software bug, Software quality, Source code and Java.
Lin Tan has researched Software bug in several fields, including Machine learning, Concurrency, Feature extraction, Artificial intelligence and Software engineering.
Her study in the fields of Component under the domain of Software engineering overlaps with other disciplines such as Security bug.
Her study in Software quality is interdisciplinary in nature, drawing from both Data mining and Database.
Her Source code research includes themes of Program analysis, Machine code, Docstring, Redundancy and Natural language.
She focuses mostly in the field of Java, narrowing it down to matters related to Program comprehension and, in some cases, Android, Static program analysis and Software development.
Her most cited work include:
- Hibernator: helping disk arrays sleep through the winter (302 citations)
- A High Throughput String Matching Architecture for Intrusion Detection and Prevention (302 citations)
- Automatically learning semantic features for defect prediction (291 citations)
What are the main themes of her work throughout her whole career to date?
Her main research concerns Software, Programming language, Software bug, Source code and Artificial intelligence.
Her Software research is multidisciplinary, relying on both Software engineering, Data mining, Linux kernel and Code.
Her studies in Software bug integrate themes in fields like Static analysis, Focus and Concurrency.
Her studies deal with areas such as Program analysis, Leverage, Docstring, Natural language and Machine translation as well as Source code.
The concepts of her Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
Her research investigates the link between Machine learning and topics such as Data modeling that cross with problems in Feature extraction.
She most often published in these fields:
- Software (44.62%)
- Programming language (30.77%)
- Software bug (27.69%)
What were the highlights of her more recent work (between 2019-2021)?
- Source code (23.08%)
- Artificial intelligence (21.54%)
- Syntax (6.15%)
In recent papers she was focusing on the following fields of study:
Source code, Artificial intelligence, Syntax, Programming language and Machine translation are her primary areas of study.
Her Source code research focuses on subjects like Software bug, which are linked to Data modeling, Feature extraction, Abstract syntax and Deep belief network.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
Her work on Program synthesis, Formal specification and Debugging as part of general Programming language study is frequently connected to Context, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her study with Software quality involves better knowledge in Software.
Her work on Software system as part of general Software research is frequently linked to Literature survey, thereby connecting diverse disciplines of science.
Between 2019 and 2021, her most popular works were:
- Deep Semantic Feature Learning for Software Defect Prediction (35 citations)
- CoCoNuT: combining context-aware neural translation models using ensemble for program repair (16 citations)
- CPC: automatically classifying and propagating natural language comments via program analysis (8 citations)
In her most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Software
Lin Tan focuses on Source code, Machine learning, Artificial intelligence, Deep learning and Natural language.
Her work deals with themes such as Semantic feature and Machine translation, which intersect with Source code.
She has included themes like Software bug, Abstract syntax, Deep belief network and Feature extraction in her Semantic feature study.
Her research in Machine translation intersects with topics in Ensemble learning, Convolutional neural network and Python.
Her Natural language study incorporates themes from Program analysis, Classifier, Code, Information retrieval and Taxonomy.
Her work often combines Task analysis and Data modeling studies.
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.
