NEZHA: Efficient Domain-Independent Differential Testing

By Theofilos Petsios, Adrian Tang, Salvatore J. Stolfo, Angelos Dennis Keromytis, Suman Sekhar Jana. 2017

137 citations recorded by the source corpus.

Subfields: Software, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Software

1. Suman Sekhar Jana
2. Kexin Pei
3. Junfeng Yang
4. Yinzhi Cao
5. Michael W. Hicks
6. Sheng Wen
7. Seyit Ahmet Camtepe
8. Xiaogang Zhu

In Signal Processing

1. Suman Sekhar Jana
2. Kexin Pei
3. Junfeng Yang
4. Yinzhi Cao
5. Sang Kil Cha
6. Michael W. Hicks
7. Sheng Wen
8. Seyit Ahmet Camtepe

Citations in the corpus

• DeepXplore: Automated Whitebox Testing of Deep Learning Systems (2017, methodology): “Since then differential testing has been widely used for successfully testing various types of traditional software including JVMs [8], C compliers [31, 61], SSL/TLS certification validation logic [6, 9, 37, 46], PDF viewers [37], space flight software [20], mobile applications [25], and Web applica”
• Fuzzing: A Survey for Roadmap (2022, background): “Fuzzing aims to generate test cases that can discover more discrepancies [138, 151].”
• The Art, Science, and Engineering of Fuzzing: A Survey (2018, background): “Several fuzzers [41], [171], [59] have used differential testing to identify discrepancies between similar programs, which are likely to indicate a bug.”
• Evaluating Fuzz Testing (2018, background): “While most papers focus on the crashes, some work changes observation to consider different classes of program behavior as interesting, e.g., longer running time [41], or differential behavior [40].”