What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Software
Data mining, Software, Software system, Artificial intelligence and World Wide Web are his primary areas of study.
His Data mining research integrates issues from Software bug, Set, Software regression, Debugging and Root cause.
The various areas that David Lo examines in his Software study include Mean reciprocal rank, Optimization problem, Software engineering and Support vector machine.
His Software system research includes elements of Software quality, Information retrieval and Database.
His Artificial intelligence research is multidisciplinary, relying on both Program comprehension and Machine learning.
The concepts of his World Wide Web study are interwoven with issues in Android, Software development and Fork.
His most cited work include:
- Where should the bugs be fixed? - more accurate information retrieval-based bug localization based on bug reports (347 citations)
- A discriminative model approach for accurate duplicate bug report retrieval (242 citations)
- Towards more accurate retrieval of duplicate bug reports (225 citations)
What are the main themes of his work throughout his whole career to date?
David Lo focuses on Software, Data mining, Artificial intelligence, Machine learning and Information retrieval.
David Lo has included themes like Software engineering, World Wide Web and Java in his Software study.
His Software engineering research is multidisciplinary, incorporating elements of Software maintenance and Software quality.
His Data mining research integrates issues from Process, Software system, Set, Debugging and Specification mining.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Identification, Empirical research and Natural language processing.
His Information retrieval study incorporates themes from Stack overflow, Code, Natural language and Source code.
He most often published in these fields:
- Software (28.10%)
- Data mining (23.06%)
- Artificial intelligence (22.87%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (22.87%)
- Code (14.73%)
- Software (28.10%)
In recent papers he was focusing on the following fields of study:
David Lo mainly focuses on Artificial intelligence, Code, Software, Empirical research and Software engineering.
His research integrates issues of Stability, Machine learning, Structure and Natural language processing in his study of Artificial intelligence.
His Code research includes elements of Identification, Java, Information retrieval, Task and Source code.
David Lo focuses mostly in the field of Software, narrowing it down to topics relating to Knowledge management and, in certain cases, Software development.
The various areas that he examines in his Empirical research study include README, Open source, World Wide Web and Key.
His Software engineering study which covers Android that intersects with Readability.
Between 2019 and 2021, his most popular works were:
- Perceptions, Expectations, and Challenges in Defect Prediction (42 citations)
- Deep code comment generation with hybrid lexical and syntactical information (29 citations)
- How does Machine Learning Change Software Development Practices (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Operating system
David Lo mainly investigates Software, Code, Artificial intelligence, Software engineering and Software bug.
He studies Software, namely Software development.
The concepts of his Code study are interwoven with issues in Java, Information retrieval, Blockchain, Commit and Source code.
His research in Artificial intelligence intersects with topics in Machine learning and Identification.
His Software engineering research incorporates themes from Feature engineering, Program transformation, Android and Smart contract.
His Software bug study combines topics in areas such as Contrast, Applied psychology and Interval.
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