What is he best known for?
The fields of study he is best known for:
- Programming language
- Operating system
- Software
Matthew B. Dwyer focuses on Programming language, Model checking, Software system, Theoretical computer science and Correctness.
Program analysis, Java, Source code, Program slicing and Software are the core of his Programming language study.
His Java study combines topics from a wide range of disciplines, such as Formal verification and Software engineering, Formal specification.
His research on Model checking also deals with topics like
- Finite-state machine that intertwine with fields like Executable,
- Temporal logic which connect with Deadlock.
His Software system study incorporates themes from Distributed computing and Implementation.
His Correctness research is multidisciplinary, incorporating elements of Data flow diagram, Data-flow analysis and Static analysis, Shape analysis.
His most cited work include:
- Patterns in property specifications for finite-state verification (1175 citations)
- Bandera: extracting finite-state models from Java source code (1025 citations)
- Property specification patterns for finite-state verification (384 citations)
What are the main themes of his work throughout his whole career to date?
Programming language, Model checking, Software, Software engineering and Software system are his primary areas of study.
His Model checking study deals with Source code intersecting with Program slicing.
The study incorporates disciplines such as Reliability engineering and Data-flow analysis in addition to Software.
In general Software engineering study, his work on Formal methods and Component often relates to the realm of Context, thereby connecting several areas of interest.
His Software system study integrates concerns from other disciplines, such as Distributed computing and Implementation.
In Java, Matthew B. Dwyer works on issues like Formal specification, which are connected to Formal verification.
He most often published in these fields:
- Programming language (36.10%)
- Model checking (30.73%)
- Software (23.90%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (6.83%)
- Software engineering (22.44%)
- Symbolic execution (10.73%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Software engineering, Symbolic execution, Machine learning and Artificial neural network.
His research in Software engineering intersects with topics in Requirements engineering, Engineering ethics and Current.
Matthew B. Dwyer has researched Symbolic execution in several fields, including Model checking, Theoretical computer science and Program analysis.
His Machine learning research includes elements of User experience design and Software, Fuzz testing, Code coverage.
In the subject of general Software, his work in Software bug is often linked to Sample, thereby combining diverse domains of study.
Natural language requirements is the focus of his Programming language research.
Between 2015 and 2021, his most popular works were:
- Code search with input/output queries (25 citations)
- On the techniques we create, the tools we build, and their misalignments: a study of KLEE (11 citations)
- SymInfer: inferring program invariants using symbolic states (10 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Operating system
- Software
His primary areas of study are Artificial intelligence, Symbolic execution, Function, Machine learning and Theoretical computer science.
His Artificial intelligence research incorporates elements of Software engineering and State.
His Symbolic execution research is classified as research in Programming language.
He has included themes like Finite-state machine, Variety and Control theory in his Function study.
When carried out as part of a general Machine learning research project, his work on Recommender system is frequently linked to work in Transformation, Set and MovieLens, therefore connecting diverse disciplines of study.
His studies in Theoretical computer science integrate themes in fields like Inference and Local variable.
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