What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- GNU/Linux
Barton P. Miller mainly focuses on Debugging, Operating system, Distributed computing, Instrumentation and Programming language.
The concepts of his Debugging study are interwoven with issues in Shared memory, Parallel computing, Tracing and Identification.
As part of his studies on Operating system, Barton P. Miller often connects relevant subjects like Mobile agent.
His study in Distributed computing is interdisciplinary in nature, drawing from both Extensibility, Scalability and Theoretical computer science.
His research in Instrumentation intersects with topics in Computer hardware, Embedded system, Overhead and Code.
His research in the fields of Compiler and Profiling overlaps with other disciplines such as Stack trace.
His most cited work include:
- The Paradyn parallel performance measurement tool (788 citations)
- An empirical study of the reliability of UNIX utilities (769 citations)
- What are race conditions?: Some issues and formalizations (392 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Debugging, Operating system, Distributed computing, Programming language and Instrumentation.
His Debugging study incorporates themes from Software engineering, Tracing, Programmer and Parallel computing.
Operating system is closely attributed to Embedded system in his research.
Barton P. Miller works mostly in the field of Distributed computing, limiting it down to topics relating to Scalability and, in certain cases, Multicast, Overlay network, Middleware and Set, as a part of the same area of interest.
His Unix research is multidisciplinary, incorporating perspectives in Random testing, Workstation and Fuzz testing.
Barton P. Miller combines subjects such as Real-time computing and Host with his study of Process.
He most often published in these fields:
- Debugging (17.11%)
- Operating system (17.11%)
- Distributed computing (15.35%)
What were the highlights of his more recent work (between 2012-2021)?
- Computer security (10.96%)
- Software (7.02%)
- Software engineering (11.84%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Computer security, Software, Software engineering, Vulnerability assessment and Software assurance.
His Computer security research includes themes of Center of excellence, Service and Secure coding.
His work on Software system as part of general Software study is frequently linked to Process, Development and Empirical research, therefore connecting diverse disciplines of science.
Barton P. Miller has researched Software engineering in several fields, including Pointer, Relevance, Scripting language, Unix and Fuzz testing.
His Computer engineering study combines topics from a wide range of disciplines, such as Debugging and Reduction.
The study incorporates disciplines such as Construct and Distributed computing in addition to Reduction.
Between 2012 and 2021, his most popular works were:
- Binary-code obfuscations in prevalent packer tools (81 citations)
- Binary code is not easy (60 citations)
- Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes (38 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- GNU/Linux
Barton P. Miller focuses on Code, Data mining, Distributed computing, Binary code and Instrumentation.
His Data mining study integrates concerns from other disciplines, such as Titan and Cluster analysis.
His Distributed computing study combines topics in areas such as Data transmission, Class, Reduction, Graphics processing unit and Central processing unit.
His research on Binary code also deals with topics like
- Binary analysis that connect with fields like Computer security and Malware,
- Identification which connect with Key.
His biological study deals with issues like Data-flow analysis, which deal with fields such as Middleware and Software engineering.
The study incorporates disciplines such as Programming language, Source code, Code coverage, Dead code and KPI-driven code analysis in addition to Theoretical computer science.
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