What is she best known for?
The fields of study she is best known for:
- Programming language
- Software
- Artificial intelligence
Corina S. Pasareanu focuses on Software, Model checking, Programming language, Symbolic execution and Java.
Her work deals with themes such as Temporal logic and Concurrency, which intersect with Software.
Corina S. Pasareanu interconnects Software verification and validation, Software system and Implementation in the investigation of issues within Model checking.
The Symbolic execution study which covers Program analysis that intersects with Machine code, Application domain and Invariant.
Her research in Java intersects with topics in Software engineering and Source code.
Her research integrates issues of Finite-state machine, Correctness and Executable in her study of Source code.
Her most cited work include:
- Bandera: extracting finite-state models from Java source code (1025 citations)
- Symbolic execution for software testing in practice: preliminary assessment (262 citations)
- Assumption generation for software component verification (173 citations)
What are the main themes of her work throughout her whole career to date?
Corina S. Pasareanu mostly deals with Symbolic execution, Programming language, Theoretical computer science, Model checking and Software.
Her work deals with themes such as Program analysis, Probabilistic logic, Artificial intelligence and Algorithm, which intersect with Symbolic execution.
Her Systems analysis research extends to the thematically linked field of Programming language.
Her work carried out in the field of Model checking brings together such families of science as Software system, Automated theorem proving and Implementation.
Her work in Software system tackles topics such as Source code which are related to areas like Computer engineering.
She has included themes like Domain, Software engineering and Test case in her Software study.
She most often published in these fields:
- Symbolic execution (38.68%)
- Programming language (30.19%)
- Theoretical computer science (29.25%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial neural network (21.70%)
- Artificial intelligence (21.70%)
- Symbolic execution (38.68%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Artificial neural network, Artificial intelligence, Symbolic execution, Robustness and Theoretical computer science.
Her Artificial neural network research is multidisciplinary, incorporating perspectives in Control, Scalability and Partition.
When carried out as part of a general Artificial intelligence research project, her work on Parsing and Probabilistic logic is frequently linked to work in Context, therefore connecting diverse disciplines of study.
Her Symbolic execution research is multidisciplinary, incorporating elements of Security analysis, Program analysis and Software engineering, Load testing.
Her work on Regular language as part of general Theoretical computer science study is frequently linked to Feed forward, Vulnerability assessment, Equivalence and Asynchronous communication, bridging the gap between disciplines.
She combines Programming language and Badger in her research.
Between 2018 and 2021, her most popular works were:
- On reliability of patch correctness assessment (23 citations)
- Property inference for deep neural networks (20 citations)
- DifFuzz: differential fuzzing for side-channel analysis (20 citations)
In her most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Software
Her primary areas of investigation include Artificial neural network, MNIST database, Artificial intelligence, Robustness and Theoretical computer science.
Her Artificial neural network study incorporates themes from Boolean satisfiability problem, Scalability, Parallelizable manifold and Parallel computing.
The various areas that Corina S. Pasareanu examines in her MNIST database study include Program analysis, Symbolic execution, Identification, Contextual image classification and Symbolic data analysis.
In her study, she carries out multidisciplinary Program analysis and Context research.
Her studies in Artificial intelligence integrate themes in fields like Machine learning and Set.
She combines Theoretical computer science and Execution time in her studies.
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