Computer Science > Software Engineering
[Submitted on 5 Sep 2018]
Title:Synthesizing Adaptive Test Strategies from Temporal Logic Specifications
View PDFAbstract:Constructing good test cases is difficult and time-consuming, especially if the system under test is still under development and its exact behavior is not yet fixed. We propose a new approach to compute test strategies for reactive systems from a given temporal logic specification using formal methods. The computed strategies are guaranteed to reveal certain simple faults in every realization of the specification and for every behavior of the uncontrollable part of the system's environment. The proposed approach supports different assumptions on occurrences of faults (ranging from a single transient fault to a persistent fault) and by default aims at unveiling the weakest one. Based on well-established hypotheses from fault-based testing, we argue that such tests are also sensitive for more complex bugs. Since the specification may not define the system behavior completely, we use reactive synthesis algorithms with partial information. The computed strategies are adaptive test strategies that react to behavior at runtime. We work out the underlying theory of adaptive test strategy synthesis and present experiments for a safety-critical component of a real-world satellite system. We demonstrate that our approach can be applied to industrial specifications and that the synthesized test strategies are capable of detecting bugs that are hard to detect with random testing.
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.