A Novel Mutation Analysis-Based Approach for Testing Parallel Behavioural Scenarios in Multi-Agent Systems

Abstract

In this work, we propose a new test case generation approach that can cover behavioural scenarios individually in a multi-agent system. The purpose is to identify, in the case of the detection of an error, the scenario that caused the detected error, among the scenarios running in parallel. For this, the approach used, in the first stage, the technique of mutation analysis and parallel genetic algorithms to identify the situations in which the agents perform the interactions, presented in the sequence diagram, of the scenario under test only; these situations will be considered as inputs of the test case. In the second stage, the approach used the activities presented in the activity diagram to identify the outputs of the test case expected for its inputs. Subsequently, the generated test cases will be used for the detection of possible errors. The proposed approach is supported by a formal framework in order to automate its phases, and it is applied to a concrete case study to illustrate and demonstrate its usefulness.