A Comparative Study of Artificial Neural Networks and Info-Fuzzy Networks as Automated Oracles in Software Testing

Agarwal, D.; Tamir, Dan E.; Kandel, Abraham

doi:10.1109/tsmca.2012.2183590

Cited by 22 publications

(19 citation statements)

References 29 publications

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“…Neural network is trained by backpropagation algorithm and training is based on black-box testing Two-layer neural network has been used in order to detect the faults in mutated code Aggarwal et al [1] The work compares artificial neural network and info fuzzy networks as automated oracles on the basis of ROC curves, training time, and dispersion analysis…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Network-Based Automated Priority Assigner

Bhasin

Khanna²,

Sharma

2015

Advances in Intelligent Systems and Computing

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The testing of a system starts with the crafting of test cases. Not all the test cases are, however, equally important. The test cases can be prioritized using policies discussed in the work. The work proposes a neural network model to prioritize the test cases. The work has been validated using backpropagation neural network. 200 test cases were crafted and the experiment was carried out using 2, 5, 10, 15, and 20 layers neural network. The results have been reported and lead to the conclusion that neural network-based priority analyzer can predict the priority of a test.

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Section: Literature Reviewmentioning

confidence: 99%

Neural Network-Based Automated Priority Assigner

Bhasin

Khanna²,

Sharma

2015

Advances in Intelligent Systems and Computing

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“…Whether the results of these testing processes are successful or not, depends greatly on how the test process is conducted. Software failure caused by human errors in software faults is a deviation of the software from its expected functions [1]. Since successful manual software testing requires assigning a great deal of manpower that is not only expensive but also time consuming, software testers are highly in favor of automated testing approaches.…”

mentioning

confidence: 99%

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

Yousif

Shahamiri

Mustafa

2015

2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA)

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One of the key software development activities that ensures software quality is software testing that needs automation due to scarcity of resources in software production; however, automating the testing process faces several issues especially those issues associated with automated test oracles. Test oracles offer simple and reliable sources of expected software behavior that guide testers to undertake the testing process and detect faults. This paper performs a comparative study on two existing test oracles using a black-box approach. We compare experimental studies, processes, and evaluation procedures reported so far. The two test oracles are Multi-network oracles based on ANNs, and IFNbased regression tester. ANN-based oracles have the capability of processing complex relationships while IFN is a test oracle that is limited to one functionality, although the test oracle performs best when applied for regression testing. The results obtained from existing experiments and evaluations, and they indicate that Multi-Network oracles have a better accuracy rate of 98.26% and a minimal misclassification error rate of 1.74% compared to the IFN regression tester. Consequently, Multi-network oracles based on ANNs are more suitable, offering better quality and reliability for a software testing process.

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“…Other areas of research involve the generalization of results, i.e., whether an approach for a specific domain can be used in other domains, and how effective it is (TORSEL, 2011;VAZQUEZ et al, 2013). Comparative studies are often published to answer such questions HASHIM, 2009;AGARWAL et al, 2012). Next we brings explanations about the most studied and reported test oracle taxonomies in accordance with researchers and practitioners.…”

Section: Test Oracle Trade-offsmentioning

confidence: 95%

“…On the other hand, test designers can create test oracles from formal methods or specifications (Figure 4b) (AICHERNIG et al, 2009;ZHANG, 2003;SUBRAMANIAM, 2002;D'SOUZA;GOPINATHAN, 2006;GIANNAKOPOULOU et al, 2011). Regarding most sophisticated cases, test oracles can place into service test data inputs to derive expected outputs of the SUT ( Figure 4c) (SANGWAN; BHATIA; SINGH, 2011;IBRAHIM, 2010b;IBRAHIM, 2010a;VANMALI;LAST;KANDEL, 2002;RAUF et al, 2010). Finally, as we already mentioned, one can consider the human oracles, where testers can use their own knowledge about the SUT to check if outputs are in accordance with specifications ( Figure 4d) (AFSHAN; MCMINN; STEVENSON, 2013;HOOK;KELLY, 2009;HARMAN, 2010;SNEED, 1988).…”

Section: Definitions and Conceptsmentioning

confidence: 99%

“…There are a variety of ML techniques applied as oracles, such as Supervised Learning Machines (SLA): (1) Artificial Neural Networks (ANN) (AGGARWAL et al, 2004;ALMOG;HEART, 2010;JIN et al, 2008;MAO et al, 2006), (2) support vector machines (WANG et al, 2011;WANG;YAO;WU, 2011), and (3) info-fuzzy networks (AGARWAL et al, 2012;ZARAKET et al, 2012).…”

Section: Specific Taxonomiesmentioning

confidence: 99%

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Test orales for systems with complex outputs: the case of TTS systems

Oliveira¹

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A Comparative Study of Artificial Neural Networks and Info-Fuzzy Networks as Automated Oracles in Software Testing

Cited by 22 publications

References 29 publications

Neural Network-Based Automated Priority Assigner

Neural Network-Based Automated Priority Assigner

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

Test orales for systems with complex outputs: the case of TTS systems

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