Performing Software Test Oracle Based on Deep Neural Network with Fuzzy Inference System

Monsefi, Amin Karimi; Zakeri, Behzad; Samsam, Sanaz; Khashehchi, Morteza

doi:10.1007/978-3-030-33495-6_31

Cited by 11 publications

(14 citation statements)

References 23 publications

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“…Ye et al [39] and Monsefi et al [24] generate oracles for functions with integer output. Some of the cases they examine have a limited range of produced outputs (e.g., a function that predicts the length of a route).…”

Section: Application Of Machine Learningmentioning

confidence: 99%

“…Monsefi et. al [24] adopt a Deep NN, which has more input and output layers than a regular NN, with a fuzzy encoder + decoder. Finally, Zhang et.…”

Section: Application Of Machine Learningmentioning

confidence: 99%

“…In terms of evaluation, five of the studies are focused on the accuracy of the oracle in a set of cases where the ground truth is known-measuring the percentage of correct classifications [1,16,31,41] or the accuracy of the model [35]. The remaining seven used the mutation score as the evaluation metric [6,24,32,34,[37][38][39].…”

Section: Application Of Machine Learningmentioning

confidence: 99%

“…Deep learning techniques, which may utilize a high number of hidden layers, may be key in building models of more complex functions. One approach to date has utilized deep learning [24], and we would expect more to explore these techniques in the coming years. However, deep learning also introduces steep requirements on the training data that may limit its applicability [36].…”

Section: Rq6 (Challenges)mentioning

confidence: 99%

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Using machine learning to generate test oracles: a systematic literature review

Fontes

2021

Proceedings of the 1st International Workshop on Test Oracles

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Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field.Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and-most commonly-expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata-including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employedand how they are applied-the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field. CCS CONCEPTS• Software and its engineering → Software verification and validation; • Computing methodologies → Machine learning.

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Section: Application Of Machine Learningmentioning

confidence: 99%

“…Monsefi et. al [24] adopt a Deep NN, which has more input and output layers than a regular NN, with a fuzzy encoder + decoder. Finally, Zhang et.…”

Section: Application Of Machine Learningmentioning

confidence: 99%

Section: Application Of Machine Learningmentioning

confidence: 99%

Section: Rq6 (Challenges)mentioning

confidence: 99%

See 2 more Smart Citations

Using machine learning to generate test oracles: a systematic literature review

Fontes

2021

Proceedings of the 1st International Workshop on Test Oracles

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“…Changing traditional numerical methods to alternative meshless approaches, such as machine learning, has become increasingly popular in recent years. Particularly in the event of problems with complex mathematical formulation, machine learning schemes are replaced by classical models [11]. Oquab et al [13] have used a weakly supervised convolutional neural network to identify objects in image processing to reduce the number of input labelled images.…”

Section: Related Workmentioning

confidence: 99%

Solving partial differential equations by a supervised learning technique, applied for the reaction–diffusion equation

et al. 2019

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Deep learning is a crucial point of valuable intelligence resources to deal with complicated mathematical problems. The effectiveness of deep learning in solving differential equations has been considered over the past few years. Supervision of the learning process requires significant information to be marked in order to train the network. Nevertheless, this approach could not be a helpful strategy in case of unknown differential equations that we have no identified data. In order to address this problem, a new method for solving differential equations will be introduced in this paper using only the boundary and initial conditions. As an efficient method, inadequate monitoring can provide an ideal bed to fix boundary and initial value issues. For verification of the proposed method, a reaction-diffusion equation was performed. This equation has a variety of applications in engineering and science.

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Machine learning‐based test oracles for performance testing of cyber‐physical systems: An industrial case study on elevators dispatching algorithms

Gartziandia

Arrieta

Ayerdi

et al. 2022

J Software Evolu Process

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The software of systems of elevators needs constant maintenance to deal with new functionality, bug fixes, or legislation changes. To automatically validate the software of these systems, a typical approach in industry is to use regression oracles, which execute test inputs both in the software version under test and in a previous software version. However, these practices require a long test execution time and cannot be re‐used at different test phases. To deal with these issues, we propose Dispatching AlgoRIthm Oracle (DARIO), a test oracle that relies on regression machine‐learning algorithms to detect both functional and non‐functional problems of the system. The machine‐learning algorithms of this oracle are trained by using data from previously tested versions to predict reference functional and non‐functional performance values of the new versions. An empirical evaluation with an industrial case study demonstrates the feasibility of using our approach. A total of five regression learning algorithms were validated by using mutation testing techniques. For the context of functional bugs, the accuracy when predicting verdicts by DARIO ranged between 95% and 98%, across the different scenarios proposed. For the context of non‐functional bugs, were competitive too, having an accuracy when predicting verdicts by DARIO ranged between 83% and 87%.

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Performing Software Test Oracle Based on Deep Neural Network with Fuzzy Inference System

Cited by 11 publications

References 23 publications

Using machine learning to generate test oracles: a systematic literature review

Using machine learning to generate test oracles: a systematic literature review

Solving partial differential equations by a supervised learning technique, applied for the reaction–diffusion equation

Machine learning‐based test oracles for performance testing of cyber‐physical systems: An industrial case study on elevators dispatching algorithms

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