Kai Chen

Professor Kai Chen received his Ph.D. degree in the University of Chinese Academy of Science in 2010; then he joined the Chinese Academy of Science in January 2010. He became the Associate Professor in September 2012 and became the full Professor in October 2015.

His research interests include software analysis and testing; smartphones and privacy.

AURC: Detecting Errors in Program Code and Documentation

We present AURC, a static framework for detecting code bugs of incorrect return checks and document defects. We observe that three objects participate in the API invocation, the document, the caller (code that invokes API), and the callee (the source code of API). Mutual corroboration of these three objects boosts the detection of code and documentation errors. We evaluated AURC on ten popular codebases. AURC discovered 529 new bugs and 224 new document defects. Maintainers acknowledge our findings and have accepted 222 code patches and 76 document patches. [CODE]

Our paper was accepted by the 32nd USENIX Security Symposium (USENIX Security 2023). [PDF]

CarpetFuzz: Automatic Program Option Constraint Extraction from Documentation for Fuzzing

We proposed a novel technique for identifying and extracting constraints among program options from the documentation. To the best of our knowledge, this is the first study that tries to use NLP to automatically figure out the relationships among program options from the documentation. With the help of this technique, AFL finds 45.97% more paths that other fuzzers cannot discover. We implemented the prototype tool, CarpetFuzz, and evaluated it on 20 popular real-world open-source programs. CarpetFuzz accurately extracted 88.85% of the relationships from their documents. Through fuzzing these programs with the valid option combinations obtained by CarpetFuzz, 57 unique crashes have been found, 30 of which have been assigned with CVE IDs.[CODE]

Our paper was accepted by the 32nd USENIX Security Symposium (USENIX Security 2023). [PDF]

A Data-free Backdoor Injection Approach in Neural Networks

We propose a novel backdoor injection approach in a "data-free" manner. We design a novel loss function for fine-tuning the original model into the backdoored one using the substitute data that is irrelevant to the main task, and optimize the fine-tuning to balance the backdoor injection and the performance on the main task. We conduct extensive experiments on various deep learning scenarios, and the evaluation results demonstrate that our data-free backdoor injection approach can efficiently embed backdoors with a nearly 100% attack success rate. [CODE]

Our paper was accepted by the 32nd USENIX Security Symposium (USENIX Security 2023). [PDF]

Achieving Accuracy and Scalability Simultaneously in Detecting Application Clones on Android Market (TO BE UPDATED)

Besides traditional problems such as potential bugs, (smartphone) application clones on Android markets bring new threats. Existing techniques achieve either accuracy or scalability, but not both. To solve those problems, we use a geometry characteristic, called centroid, of dependency graphs to measure the similarity between methods (code fragments) in two apps. [demo]

Our paper was accepted by the 36th International Conference on Software Engineering (ICSE 2014). [PDF]