A Single-Network ANN-based Oracle to verify logical software modules

Shahamiri, Seyed Reza; Kadir, Wan Hazli Wan; Ibrahim, Suhaimi

doi:10.1109/icste.2010.5608808

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…Among scalar estimators, MSE is the most commonly used cost that has been used for back-propagation algorithm. Similarly, most of the previous studies on ANN-based oracles have cited MSE as the performance function [7,8,[42][43][44].…”

Section: Inputsmentioning

confidence: 99%

“…The misclassification error can be presented by MSE, 8 which it is the squared difference between the training sample outputs and the results generated by the network. According to [49], MSE can be considered to show how well the ANN outputs fit the expected outputs.…”

Section: Step 2: Train the Annsmentioning

confidence: 99%

“…We applied Single-Network Oracles to test decision-making structures [7] and verify complex logical modules [8]. Particularly, a Multilayered Perceptron ANN that modeled a university subjectregistration policy was trained using domain experts' knowledge of the system.…”

Section: Prominent Oraclesmentioning

confidence: 99%

“…Nonetheless, we identified some challenges regarding the oracle related activities automation [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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An automated framework for software test oracle

Shahamiri

Kadir

Ibrahim

et al. 2011

Information and Software Technology

Self Cite

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Section: Inputsmentioning

confidence: 99%

Section: Step 2: Train the Annsmentioning

confidence: 99%

Section: Prominent Oraclesmentioning

confidence: 99%

“…Nonetheless, we identified some challenges regarding the oracle related activities automation [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An automated framework for software test oracle

Shahamiri

Kadir

Ibrahim

et al. 2011

Information and Software Technology

Self Cite

View full text Add to dashboard Cite

“…We applied Single-Network Oracles to test decision-making structures (Shahamiri et al 2010a) and verify complex logical modules (Shahamiri et al 2010b). Particularly, a Multilayered Perceptron ANN that modeled a university subject-registration policy was trained using domain experts' knowledge of the system.…”

Section: Ann Based Single-network Oraclesmentioning

confidence: 99%

Artificial neural networks as multi-networks automated test oracle

et al. 2011

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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.

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The integration of machine learning into automated test generation: A systematic mapping study

Fontes

Gay

2023

Software Testing Verif & Rel

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Machine learning (ML) may enable effective automated test generation. We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges in this intersection by performing. We perform a systematic mapping study on a sample of 124 publications. ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property‐based, and expected output oracles. Supervised learning—often based on neural networks—and reinforcement learning—often based on Q‐learning—are common, and some publications also employ unsupervised or semi‐supervised learning. (Semi‐/Un‐)Supervised approaches are evaluated using both traditional testing metrics and ML‐related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. The work‐to‐date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed—and how they are applied—benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.

show abstract

A Single-Network ANN-based Oracle to verify logical software modules

Cited by 15 publications

References 14 publications

An automated framework for software test oracle

An automated framework for software test oracle

Artificial neural networks as multi-networks automated test oracle

The integration of machine learning into automated test generation: A systematic mapping study

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