Abstract
Modern RESTful services expose RESTful APIs to integrate with diversified applications. Most RESTful API parameters are weakly typed, which greatly increases the possible input value space. Weakly-typed parameters pose difficulties for automated testing tools to generate effective test cases to reveal web service defects related to parameter validation. We call this phenomenon the type collapse problem. To remedy this problem, we introduce FET (Format-encoded Type) techniques, including the FET, the FET lattice, and the FET inference to model fine-grained information for API parameters. Inferred FET can enhance parameter validation, such as generating a parameter validator for a certain RESTful server. Enhanced by FET techniques, automated testing tools can generate targeted test cases. We demonstrate Leif, a trace-driven fuzzing tool, as a proof-of-concept implementation of FET techniques. Experiment results on 27 commercial services show that FET inference precisely captures documented parameter definitions, which helps Leif discover 11 new bugs and reduce <inline-formula><tex-math notation="LaTeX">$72\% - 86\%$</tex-math></inline-formula> fuzzing time compared to state-of-the-art fuzzers. Leveraged by the inter-parameter dependency inference, Leif saves 15% fuzzing time.
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