Simon J. Gibbs

Artificial Intelligence · 2013

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Simon J. Gibbs trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Julian Seward (in Artificial Intelligence)
  2. Nicholas Nethercote (in Artificial Intelligence)

Who trusts Simon J. Gibbs

  1. Erik Krogh Kristensen (in Information Systems)
  2. Anders Møller (in Information Systems)

Papers from Simon J. Gibbs's perspective

Ordered by how strongly the researchers Simon J. Gibbs trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Dynamic Taint Analysis for Automatic Detection, Analysis, and SignatureGeneration of Exploits on Commodity Software (Network and Distributed System Security Symposium)
  2. Using Valgrind to Detect Undefined Value Errors with Bit-Precision (USENIX ATC, General Track)
  3. DART: directed automated random testing (ACM-SIGPLAN Symposium on Programming Language Design and Implementation)
  4. Pin: building customized program analysis tools with dynamic instrumentation (ACM-SIGPLAN Symposium on Programming Language Design and Implementation)
  5. Dytan: a generic dynamic taint analysis framework (International Symposium on Software Testing and Analysis)
  6. Valgrind: a framework for heavyweight dynamic binary instrumentation (ACM-SIGPLAN Symposium on Programming Language Design and Implementation)
  7. A Symbolic Execution Framework for JavaScript (IEEE Symposium on Security and Privacy)
  8. Towards Type Inference for JavaScript
  9. An analysis of the dynamic behavior of JavaScript programs (ACM-SIGPLAN Symposium on Programming Language Design and Implementation)
  10. Efficient, transparent, and comprehensive runtime code manipulation

Their papers