What is he best known for?
The fields of study he is best known for:
- Programming language
- Operating system
- Software
Michael D. Ernst mainly investigates Programming language, Theoretical computer science, Formal specification, Java and Invariant.
His Programming language study typically links adjacent topics like Software engineering.
His work in the fields of Theoretical computer science, such as Satisfiability, overlaps with other areas such as Suite.
His Java research integrates issues from Debugging and Interface.
The study incorporates disciplines such as Binary form, Data structure and Software evolution in addition to Invariant.
Michael D. Ernst usually deals with Data structure and limits it to topics linked to Test suite and Unit testing, Algorithm and Set.
His most cited work include:
- Dynamically Discovering Likely Program Invariants to Support Program Evolution (923 citations)
- The Daikon system for dynamic detection of likely invariants (806 citations)
- An overview of JML tools and applications (657 citations)
What are the main themes of his work throughout his whole career to date?
Michael D. Ernst mainly focuses on Programming language, Java, Theoretical computer science, Software engineering and Software.
His Programming language research is multidisciplinary, relying on both Invariant and Code.
His Invariant study combines topics in areas such as Formal specification and Software evolution.
He combines subjects such as Type, Concurrency and Code refactoring with his study of Java.
Michael D. Ernst interconnects Algorithm, Correctness, Inference, Solver and Data structure in the investigation of issues within Theoretical computer science.
As a member of one scientific family, Michael D. Ernst mostly works in the field of Software, focusing on Debugging and, on occasion, Software bug.
He most often published in these fields:
- Programming language (40.26%)
- Java (23.81%)
- Theoretical computer science (17.75%)
What were the highlights of his more recent work (between 2015-2021)?
- Programming language (40.26%)
- Software (12.55%)
- Code (10.82%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Programming language, Software, Code, Data mining and Distributed computing.
His research combines Test case and Programming language.
Within one scientific family, Michael D. Ernst focuses on topics pertaining to Source code under Software, and may sometimes address concerns connected to Data security, Encryption and Certificate.
His Code research includes elements of Computing Methodologies and Line.
The concepts of his Data mining study are interwoven with issues in Software bug, Probabilistic logic, Debugging and Set.
His Distributed computing research focuses on Debugger and how it connects with Asynchrony, Model checking, State space, Exploratory testing and Node.
Between 2015 and 2021, his most popular works were:
- Evaluating and improving fault localization (177 citations)
- Planning for change in a formal verification of the raft consensus protocol (66 citations)
- An Empirical Study of Fault Localization Families and Their Combinations (50 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Operating system
- Software
The scientist’s investigation covers issues in Debugging, Software, Theoretical computer science, Domain and Test case.
His studies deal with areas such as Software bug, Nexus and Data mining as well as Debugging.
Many of his studies on Data mining involve topics that are commonly interrelated, such as Algorithm.
His Theoretical computer science study incorporates themes from Mathematical proof, State, Data structure and Task.
His Test case research includes themes of Javadoc, Programming language, Software development and Natural language.
His studies in Software development integrate themes in fields like Java, Semantics and Executable.
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.
