On the Expected Number of Failures Detected by Subdomain Testing and Random Testing
IEEE Transactions on Software Engineering
A Framework for Specification-Based Testing
IEEE Transactions on Software Engineering
Using Test Oracles Generated from Program Documentation
IEEE Transactions on Software Engineering
Black-box test reduction using input-output analysis
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Automated test oracles for GUIs
SIGSOFT '00/FSE-8 Proceedings of the 8th ACM SIGSOFT international symposium on Foundations of software engineering: twenty-first century applications
On Comparisons of Random, Partition, and Proportional Partition Testing
IEEE Transactions on Software Engineering
Software testing using model programs
Software—Practice & Experience
Software Testing: A Craftman's Approach
Software Testing: A Craftman's Approach
Software Testing
Artificial Neural Networks
What Is Software Testing? And Why Is It So Hard?
IEEE Software
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Automated Software Test Data Generation for Complex Programs
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Generating Expected Results for Automated Black-Box Testing
Proceedings of the 17th IEEE international conference on Automated software engineering
Predicting Testability of Program Modules Using a Neural Network
ASSET '00 Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSET'00)
Model-based Testing of a Highly Programmable System
ISSRE '98 Proceedings of the The Ninth International Symposium on Software Reliability Engineering
The Art of Software Testing
A neural net based approach to Test Oracle
ACM SIGSOFT Software Engineering Notes
Artificial Intelligence Methods In Software Testing (Series in Machine Perception & Artifical Intelligence " Vol. 56)
Automating regression testing for evolving GUI software: Research Articles
Journal of Software Maintenance and Evolution: Research and Practice - 2003 International Conference on Software Maintenance: The Architectural Evolution of Systems
Designing and comparing automated test oracles for GUI-based software applications
ACM Transactions on Software Engineering and Methodology (TOSEM)
Journal of Systems and Software
Introduction to Software Testing
Introduction to Software Testing
Automatic, evolutionary test data generation for dynamic software testing
Journal of Systems and Software
Building test cases and oracles to automate the testing of web database applications
Information and Software Technology
Automating regression test selection based on UML designs
Information and Software Technology
Artificial Neural Network for Automatic Test Oracles Generation
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 02
Software Testing: Fundamental Principles and Essential Knowledge
Software Testing: Fundamental Principles and Essential Knowledge
A Comparative Study on Automated Software Test Oracle Methods
ICSEA '09 Proceedings of the 2009 Fourth International Conference on Software Engineering Advances
Guide to Advanced Software Testing
Guide to Advanced Software Testing
Neural networks based automated test oracle for software testing
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Artificial neural networks as multi-networks automated test oracle
Automated Software Engineering
Assessing test adequacy for black-box systems without specifications
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
Artificial neural networks as multi-networks automated test oracle
Automated Software Engineering
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Context: One of the important issues of 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 that produced by the software. However, in order to generate an automated test oracle, oracle challenges need to be addressed. These challenges are output-domain generation, input domain to output domain mapping, and a comparator to decide on the accuracy of the actual outputs. Objective: This paper proposes an automated test oracle framework to address all of these challenges. Method: I/O Relationship Analysis is used to generate the output domain automatically and Multi-Networks Oracles based on artificial neural networks are introduced to handle the second challenge. The last challenge is addressed using an automated comparator that adjusts the oracle precision by defining the comparison tolerance. The proposed approach was evaluated using an industry strength case study, which was injected with some faults. The quality of the proposed oracle was measured by assessing its accuracy, precision, misclassification error and practicality. Mutation testing was considered to provide the evaluation framework by implementing two different versions of the case study: a Golden Version and a Mutated Version. Furthermore, a comparative study between the existing automated oracles and the proposed one is provided based on which challenges they can automate. Results: Results indicate that the proposed approach automated the oracle generation process 97% in this experiment. Accuracy of the proposed oracle was up to 98.26%, and the oracle detected up to 97.7% of the injected faults. Conclusion: Consequently, the results of the study highlight the practicality of the proposed oracle in addition to the automation it offers.