An empirical study of the robustness of Windows NT applications using random testing
By J. Forrester, B. Miller. 2000
331 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/4625d647f85f7e1a04c5386aaed0b48fa27c6c03
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Koushik Sen
- Rupak Majumdar
- Jacob Burnim
- Gordon Fraser
- Carlos Pacheco
- Michael D. Ernst
- Patrice Godefroid
- Thomas Ball
Citations in the corpus
- JCrasher: an automatic robustness tester for Java (2004, background): “For example, Windows GUI applications are provided random streams of keystrokes, mouse events and Win32 messages [3].”
- Heuristics for Scalable Dynamic Test Generation (2008, methodology): “A simple and effective technique for automated test generation is random testing [2, 22, 9, 4, 6, 23] (a.”
- CUTE: a concolic unit testing engine for C (2005, background): “nique is to randomly choose the values over the domain of potential inputs [4,8,10,21].”
- DART: directed automated random testing (2005, background): “, [11]), and also makes it much more effective in finding errors thanks to the use of dynamic test generation to drive the program along alternative conditional branches.”
- Feedback-Directed Random Test Generation (2007, methodology): “Random testing [14] has been used to find errors in many applications; a partial list includes Unix utilities [19], Windows GUI applications [10], Haskell programs [2], and Java programs [3, 23, 22].”
- Automated Whitebox Fuzz Testing (2008, background): “Please contact the authors to check for an updated version before citing or redistributing. data [11, 28, 1, 4].”
- Hybrid Concolic Testing (2007, background): “The simplest, and yet often very effective, techniques use random generation of (concrete) te st inputs [3, 22, 12, 5, 7, 23, 20].”
- Grammar-based whitebox fuzzing (2008, unknown): “It simply consis t in randomly modifying well-formed inputs and testing the resulting variants [13, 3, 12].”
- Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations (2019, methodology): “[132] applied a fuzzy technique [190] to ZigBee networks to locate and analyze vulnerabilities within IoT networks.”