Assurances in Software Testing: A Roadmap

TL;DR

This paper advocates for a shift in software testing towards quantifying and providing statistical guarantees on testing assurances, especially in fuzzing, to better inform stopping criteria and residual risk assessment.

Contribution

It introduces a general framework for quantifying testing assurances and discusses future research directions for integrating statistical methods into fuzzing.

Findings

Framework for statistical guarantees in fuzzing

Identification of key open research challenges

Proposal for risk quantification methods

Abstract

As researchers, we already understand how to make testing more effective and efficient at finding bugs. However, as fuzzing (i.e., automated testing) becomes more widely adopted in practice, practitioners are asking: Which assurances does a fuzzing campaign provide that exposes no bugs? When is it safe to stop the fuzzer with a reasonable residual risk? How much longer should the fuzzer be run to achieve sufficient coverage? It is time for us to move beyond the innovation of increasingly sophisticated testing techniques, to build a body of knowledge around the explication and quantification of the testing process, and to develop sound methodologies to estimate and extrapolate these quantities with measurable accuracy. In our vision of the future practitioners leverage a rich statistical toolset to assess residual risk, to obtain statistical guarantees, and to analyze the cost-benefit trade-off for ongoing fuzzing campaigns. We propose a general framework as a first starting point to tackle this fundamental challenge and discuss a large number of concrete opportunities for future research.