signatr: A Data-Driven Fuzzing Tool for R

Abstract

The fast-and-loose, permissive semantics of dynamic programming languages limit the power of static analyses. For that reason, soundness is often traded for precision through dynamic program analysis. Dynamic analysis is only as good as the available runnable code, and relying solely on test suites is fraught as they do not cover the full gamut of possible behaviors. Fuzzing is an approach for automatically exercising code, and could be used to obtain more runnable code. However, the shape of user-defined data in dynamic languages is difficult to intuit, limiting a fuzzer's reach.We propose a feedback-driven blackbox fuzzing approach which draws inputs from a database of values recorded from existing code. We implement this approach in a tool called signatr for the R language. We present the insights of its design and implementation, and assess signatr's ability to uncover new behaviors by fuzzing 4,829 R functions from 100 R packages, revealing 1,195,184 new signatures.CCS Concepts: • Software and its engineering → Software notations and tools.

Optimizing asynchronous JavaScript applications

“…It is difficult to apply an approach that relies too heavily on dynamic analysis given the current developer culture around testing, and in terms of research it is difficult to evaluate approaches that rely heavily on dynamic analysis due to the lack of easily executable code. This can be alleviated with test generation, though test generation as it applies to dynamic languages has its own set of challenges [218,47,193], mainly due to the lack of effective implicit oracles.…”