What is he best known for?
The fields of study he is best known for:
- Programming language
- Software
- Operating system
Premkumar Devanbu spends much of his time researching Software, Software quality, Software development, Data mining and Naturalness.
His Software research is multidisciplinary, incorporating elements of Task and Data science.
His research in Software quality focuses on subjects like Process, which are connected to Functional programming, Strong and weak typing, Procedural programming and Stability.
His Software development research includes themes of Python, Team diversity, Knowledge management, Social network and Software engineering.
His Software engineering research is multidisciplinary, relying on both Backporting, Software development process and Software construction.
His studies deal with areas such as Quality, Variety, Predictive modelling and Software quality control as well as Data mining.
His most cited work include:
- On the naturalness of software (528 citations)
- Software engineering for security: a roadmap (392 citations)
- LaSSIE: a knowledge-based software information system (332 citations)
What are the main themes of his work throughout his whole career to date?
Software, Software engineering, Programming language, Source code and Software quality are his primary areas of study.
Premkumar Devanbu has included themes like Language model, Reuse and Data science in his Software study.
His research integrates issues of Software development, Software development process, Description logic, Software system and Java in his study of Software engineering.
His work on Parse tree, Object-oriented programming and Static analysis as part of general Programming language study is frequently linked to Query by Example, bridging the gap between disciplines.
Premkumar Devanbu interconnects Maintainability, Grammar and Code in the investigation of issues within Source code.
His Software quality research is multidisciplinary, incorporating perspectives in Computer security, Software design pattern, Commit and Process.
He most often published in these fields:
- Software (31.76%)
- Software engineering (25.29%)
- Programming language (20.59%)
What were the highlights of his more recent work (between 2013-2021)?
- Software (31.76%)
- Programming language (20.59%)
- Source code (15.29%)
In recent papers he was focusing on the following fields of study:
Premkumar Devanbu focuses on Software, Programming language, Source code, Code and Naturalness.
The various areas that Premkumar Devanbu examines in his Software study include Java, Software engineering, Process and Data science.
Premkumar Devanbu studied Programming language and Commit that intersect with Call graph, Directory and Recommender system.
His Source code study incorporates themes from Exploit, Maintainability, Theoretical computer science and Grammar.
His studies in Code integrate themes in fields like Variable, Compiler, Natural language processing, Static analysis and Error detection and correction.
His work in Big code addresses subjects such as Machine learning, which are connected to disciplines such as Artificial intelligence.
Between 2013 and 2021, his most popular works were:
- A Survey of Machine Learning for Big Code and Naturalness (298 citations)
- A large scale study of programming languages and code quality in github (213 citations)
- Quality and productivity outcomes relating to continuous integration in GitHub (200 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Software
- Operating system
His scientific interests lie mostly in Software, Software quality, Naturalness, Knowledge management and Empirical research.
His work carried out in the field of Software brings together such families of science as Java, Data mining, Software engineering and Code.
His work on Software engineering is being expanded to include thematically relevant topics such as Data science.
The concepts of his Software quality study are interwoven with issues in Continuous integration, Bit bucket, Variables and Software construction.
His Naturalness research covers fields of interest such as Source code, Language model and Natural language.
His work focuses on many connections between Language model and other disciplines, such as Theoretical computer science, that overlap with his field of interest in Programming language.
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