This article covers the Reducing Failure-Inducing Inputs chapter from Fuzzing Book. This chapter builds on the concepts from the Fuzzing and Fuzzing With Grammars chapters. Let’s dive in to learn more about increasing the efficiency of fuzzed inputs for debugging!
This chapter highlights how testing fuzzed inputs can be unreliable when it comes time to trace the error back to its source. As such, some steps can be taken to break the inputs down and find where the error is. This is where the reductions come into play, wherein the input is split in half and each half is tested until the source of the error is determined. This can be automated using a strategy called Delta Debugging, which uses a binary search strategy to implement the reductions. The class below is built on two created previously in the chapter, the
Reducer and the
CachingReducer, which builds the functionality for the splitting and for saving the results.
class DeltaDebuggingReducer(CachingReducer): """Reduce inputs using delta debugging.""" def reduce(self, inp: str) -> str: """Reduce input `inp` using delta debugging. Return reduced input.""" self.reset() assert self.test(inp) != Runner.PASS = 2 # Initial granularity n while len(inp) >= 2: = 0.0 start = len(inp) / n subset_length = False some_complement_is_failing while start < len(inp): = inp[:int(start)] + \ complement int(start + subset_length):] inp[ if self.test(complement) == Runner.FAIL: = complement inp = max(n - 1, 2) n = True some_complement_is_failing break += subset_length start if not some_complement_is_failing: if n == len(inp): break = min(n * 2, len(inp)) n return inp
When a tester runs the
reduce method of the class presented above, they can hopefully find a minimal representation of the character(s) responsible for the error. This form of reducing the failing inputs also reduces the cognitive load on the programmer, since it is a relatively simple and straightforward form of testing. It also is, as previously highlighted, quite clear on the source of the failure. This aids in identifying duplicates of an issue as well.
Th downside is that this is not a particularly efficient approach! The best case for complexity is \(O(log_2(n))\), and the worst is \(O(n^2)\), which is similar to the simple grammar fuzzer that we explored in a previous chapter. Can we improve this approach to automated debugging?
Again similar to the simple grammar fuzzer, there is a more efficient, although more complex, strategy, called grammar-based input reduction. Using a grammar can, as previously explored, help with ensuring that the inputs are syntactically valid for the sake of a program that may outright reject inputs that cannot be parsed, or inputs where delta debugging would not be able to comply with the constraints of the input’s syntax. Using grammar-based reduction, the input can be reduced grammatically instead of via binary, either by replacing subtrees or by alternative expansion. Let’s explore these approaches in greater detail!
Replacing subtrees is done by picking a subtree from the same token and replacing one higher up with that subtree. Alternative expansion is checking for alternate children that can be chosen and would thus would led to a smaller overall tree. Combining these with a
depth parameter also facilitates further reduction, as it then only returns trees of a certain depth to refine where replacements or alternative expansions can be done.
The chapter on Reducing Failure-Inducing Inputs provides a comprehensive overview of different strategies for identifying the key parts of a fuzzed input that leads to an error during software testing. It emphasizes the need for a tailored approach, considering the unique aspects of each program and the program’s input(s). The chapter introduces two main strategies: delta debugging, a simple but effective method, and grammar-based input reduction, a more complex but efficient alternative. The choice between these depends on the balance a programmer seeks between simplicity and efficiency. The chapter serves as a valuable guide for enhancing testing skills and simplifying error detection.
Addressing the challenge posed by fuzz testing, which generates inputs triggering errors without clear localization of the faulty code,
DeltaDebuggingReducer automates the reduction process. By utilizing the
reduce function of this class, the tests for
chasten could automatically identify and isolate the character(s) responsible for errors in the input. This approach would simplify the testing process and reduces the cognitive load on the software engineer, helping us pinpoint the source of failures. However, it is essential to acknowledge of the trade-off, as the method’s efficiency is not optimal, with worst-case complexity of \(O(n^2)\). To address this limitation, a more sophisticated strategy, grammar-based input reduction, can be considered. This technique is more efficient compared to the binary search strategy of delta debugging. Ultimately, we should consider implementing an automated debugging strategy like delta debugging — this might be helpful because it would enable us to quickly and automatically find bugs in, for instance, XPath expressions that do not find any matches. It is also possible that we could apply delta debugging to the automated test and debugging of the