BMC+Fuzz : Efficient and Effective Test Generation
Ravindra Metta1, Raveendra Kumar Medicherla2 and Samarjit Chakraborty3
1TCS Research Pune, India
r.metta@tcs.com
2TCS Research Bangalore, India
raveendra.kumar@tcs.com
3Department of Computer Science University of North Carolina at Chapel Hill
samarjit@cs.unc.edu
ABSTRACT
Coverage Guided Fuzzing (CGF) is a greybox test generation technique. Bounded Model Checking (BMC) is a whitebox test generation technique. Both these have been highly successful at program coverage as well as error detection. It is well known that CGF fails to cover complex conditionals and deeply nested program points. BMC, on the other hand, fails to scale for programming features such as large loops and arrays.
To alleviate the above problems, we propose (1) to combine BMC and CGF by using BMC for a short and potentially incomplete unwinding of a given program to generate effective initial test prefixes, which are then extended into complete test inputs for CGF to fuzz, and (2) in case BMC gets stuck even for the short unwinding, we automatically identify the reason, and rerun BMC with a corresponding remedial strategy. We call this approach as BMCFuzz and implemented it in the VeriFuzz framework. This implementation was experimentally evaluated by participating in Test-Comp 2021 and the results show that BMCFuzz is both effective and efficient at covering branches as well as exposing errors. In this paper, we present the details of BMCFuzz and our analysis of the experimental results.
