Alternative Effort-optimal Model-based Strategy for State Machine Testing of IoT Systems

TL;DR

This paper introduces a new model-based testing strategy for IoT systems that produces shorter, more efficient test cases with flexible start/end states, improving over existing methods.

Contribution

The paper presents an alternative effort-optimal model-based testing approach that allows flexible test case length and start/end state marking for IoT systems.

Findings

Generates fewer, shorter test cases

Reduces test step duplications

Offers flexible test case length and start/end states

Abstract

To effectively test parts of the Internet of Things (IoT) systems with a state machine character, Model-based Testing (MBT) approach can be taken. In MBT, a system model is created, and test cases are generated automatically from the model, and a number of current strategies exist. In this paper, we propose a novel alternative strategy that concurrently allows us to flexibly adjust the preferred length of the generated test cases, as well as to mark the states, in which the test case can start and end. Compared with an intuitive N-switch coverage-based strategy that aims at the same goals, our proposal generates a lower number of shorter test cases with fewer test step duplications.