Artificial neural networks as multi-networks automated test oracle

Abstract

One of the important issues in software testing is to provide an automated test oracle. Test oracles are reliable sources of how the software under test must operate. In particular, they are used to evaluate the actual results produced by the software. However, in order to generate an automated test oracle, it is necessary to map the input domain to the output domain automatically. In this paper, Multi-Networks Oracles based on Artificial Neural Networks are introduced to handle the mapping automatically. They are an enhanced version of previous ANN-Based Oracles. The proposed model was evaluated by a framework provided by mutation testing and applied to test two industry-sized case studies. In particular, a mutated version of each case study was provided and injected with some faults. Then, a fault-free version of it was developed as a Golden Version to evaluate the capability of the proposed oracle finding the injected faults. Meanwhile, the quality of the proposed oracle is measured by assessing its accuracy, precision, misclassification error and recall. Furthermore, the results of the proposed oracle are compared with former ANN-based Oracles. Accuracy of the proposed oracle was up to 98.93%, and the oracle detected up to 98% of the injected faults. The results of the study show the proposed oracle has better quality and applicability than the previous model.

The Oracle Problem in Software Testing: A Survey

“…Memon et al [133], [134], [197] introduced and developed the GUITAR tool, which has been evaluated by treating the current version of the SUT as correct, inferring the specification, and then executing the generated test inputs. Artificial Neural Networks have also been applied to learn system behaviour and detect deviations from it [162], [163].…”

Automated Test Oracles

“…The technique also uses the correlation-based feature selection to select the attributes. In their case study, Vanmali et al and Shahamiri et al use 11 attributes related to credit card approval and 12 attributes related to care insurance to represent input and output factors, respectively [65,68].…”

Test oracles based on artificial neural networks and info fuzzy networks: A comparative study

“…Multi-Network Oracle based on ANN Multi-network oracles based on AN the latest software testing methods Shahamiri et al [6]. They are an impr the ordinary single-network oracles th to produce more accurate oracles [ network oracles is similar to the other the oracle architecture and outpu procedure.…”