Objectives: This paper presents a new hybrid ACO-NSA algorithm for the automatic test data generation problem with path coverage as an objective function. Method: In it, at first instance, (detectors) are generated ant colony optimization (ACO), and then set (detector set) has been refined by negative selection (NSA) Hamming distance. Findings: The algorithm’s performance is tested on several b...

Test Data Generation (TDG) aims at automatically obtaining test inputs which can then be used by a software testing tool to validate the functional behaviour of the program. In this paper, we propose resource-aware TDG, whose purpose is to generate test cases (from which the test inputs are obtained) with associated resource consumptions. The framework is parametric w.r.t. the notion of resourc...

Existing automated test data generation techniques tend to start from scratch, implicitly assuming no pre-existing test data are available. However, this assumption may not always hold, and where it does not, there may be a missed opportunity; perhaps the pre-existing test cases could be used to assist the automated generation of additional test cases. This paper introduces search-based test da...