Assurances in Software Testing: A Roadmap
This work tackles the problem of providing statistical assurances in software testing for practitioners, but it is incremental as it proposes a starting framework without new empirical results.
The paper addresses the challenge of quantifying assurances in software testing, particularly for fuzzing campaigns, by proposing a framework to estimate residual risk and statistical guarantees, aiming to help practitioners decide when to stop testing with measurable accuracy.
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.