What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Software
His scientific interests lie mostly in Operating system, Software bug, Concurrency, Bebugging and Software.
His Software bug research is multidisciplinary, incorporating elements of Compiler and Stack buffer overflow.
His study with Concurrency involves better knowledge in Programming language.
The Programming language study combines topics in areas such as Semantics and Dependability.
His Bebugging research includes elements of Code, Code refactoring and Code coverage.
His study in Software is interdisciplinary in nature, drawing from both Software engineering and Component.
His most cited work include:
- Learning from mistakes: a comprehensive study on real world concurrency bug characteristics (737 citations)
- CP-Miner: finding copy-paste and related bugs in large-scale software code (463 citations)
- AVIO: detecting atomicity violations via access interleaving invariants (349 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Concurrency, Software, Operating system, Distributed computing and Debugging.
His work deals with themes such as Synchronization, Multi-core processor, Thread and Bebugging, which intersect with Concurrency.
His Bebugging study deals with Code coverage intersecting with Code refactoring.
Shan Lu has researched Software in several fields, including Latency, Real-time computing, Static analysis, Embedded system and Source code.
The various areas that Shan Lu examines in his Distributed computing study include Scalability, Cloud computing, Set and Reliability.
His Debugging study integrates concerns from other disciplines, such as False positive paradox, User interface, Reliability engineering, Low overhead and Syntax.
He most often published in these fields:
- Concurrency (33.63%)
- Software (25.66%)
- Operating system (24.78%)
What were the highlights of his more recent work (between 2018-2021)?
- Software (25.66%)
- Artificial intelligence (4.42%)
- Cloud computing (10.62%)
In recent papers he was focusing on the following fields of study:
Shan Lu spends much of his time researching Software, Artificial intelligence, Cloud computing, Distributed computing and Concurrency.
His research in Software intersects with topics in Real-time computing, Static analysis, Oracle and System dynamics.
His work carried out in the field of Static analysis brings together such families of science as Software bug, Software system, Performance tuning, Operating system and Software configuration management.
His Software bug research incorporates themes from Thread and Concurrent computing.
His Cloud computing research includes themes of Linear temporal logic, Programming language, Program synthesis and Server.
His research investigates the link between Concurrency and topics such as Synchronization that cross with problems in False positive paradox, Computer engineering, Correctness and Set.
Between 2018 and 2021, his most popular works were:
- AutoTap: synthesizing and repairing trigger-action programs using LTL properties (17 citations)
- Efficient scalable thread-safety-violation detection: finding thousands of concurrency bugs during testing (11 citations)
- View-centric performance optimization for database-backed web applications (10 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Software
Shan Lu mainly investigates Distributed computing, Cloud computing, Code, Reliability and Scalability.
His Distributed computing research integrates issues from Node, Software and Taxonomy.
His research integrates issues of Linear temporal logic, Programming language and Program synthesis in his study of Cloud computing.
His Code research is multidisciplinary, relying on both Web application and Web page, HTML element.
Shan Lu combines subjects such as Synchronization, False positive paradox and Concurrency with his study of Reliability.
His work carried out in the field of Scalability brings together such families of science as Event and State.
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.
