Architecture-Guided Test Resource Allocation Via Logic

TL;DR

This paper presents a novel logic called Quantitative Confidence Logic (QCL) for quantifying confidence in proofs, and applies it to optimize test resource allocation based on system architecture, demonstrated through a new tool Astrahl.

Contribution

Introduction of QCL and a method to use it for architecture-aware test resource allocation, along with the Astrahl tool implementation.

Findings

Astrahl outperforms existing strategies in resource allocation.

QCL effectively quantifies confidence in proofs.

Architecture-aware approach improves testing efficiency.

Abstract

We introduce a new logic named Quantitative Confidence Logic (QCL) that quantifies the level of confidence one has in the conclusion of a proof. By translating a fault tree representing a system's architecture to a proof, we show how to use QCL to give a solution to the test resource allocation problem that takes the given architecture into account. We implemented a tool called Astrahl and compared our results to other testing resource allocation strategies.