Software test case generation &amp;amp; test oracle design using neural network

Majma, Negar; Babamir, Seyed Morteza

doi:10.1109/iraniancee.2014.6999712

Cited by 3 publications

(7 citation statements)

References 11 publications

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“…Of the 82 publications addressing input generation, 65 propose Black Box and 17 propose White Box approaches. White Box approaches are traditionally common in input generation, as the ‘coverage criteria’—checklists of goals [19]—that are the focus of White Box testing offer measurable test generation targets [5]. Such approaches benefit from the inclusion of ML [5].…”

Section: Resultsmentioning

confidence: 99%

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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.

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Section: Resultsmentioning

confidence: 99%

“…Another concern is achieving code coverage. Majma et al use supervised learning for both input and oracle generation [19]. A model associates inputs with paths through the source code, then generates new inputs that execute uncovered paths.…”

Section: System Test Generationmentioning

confidence: 99%

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

Fontes

Gay

2023

Software Testing Verif & Rel

View full text Add to dashboard Cite

show abstract

“…Of the 65 publications addressing input generation, 53 propose Black Box and 12 propose White Box approaches. White Box approaches are traditionally common in input generation, as the "coverage criteria"-checklists of goals [13]-that are the focus of White Box testing offer measurable optimization targets [5]. Such approaches can benefit from the inclusion of ML [5].…”

Section: Test Input Generationmentioning

confidence: 99%

“…Another concern is achieving code coverage. [13] use neural networks (NNs) for input and oracle generation. A NN associates inputs with paths through the source code, then generates inputs that execute uncovered paths.…”

Section: Examining Specific Practicesmentioning

confidence: 99%

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The Integration of Machine Learning into Automated Test Generation: A Systematic Literature Review

Fontes¹,

Gay²

2022

Preprint

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Context: Machine learning (ML) may enable effective automated test generation.Objectives: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges.Methods: We perform a systematic literature review on a sample of 97 publications.Results: 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. Conclusion: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.

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An approach to design test oracle for aspect oriented software systems using soft computing approach

Singhal

Bansal

Kumar

2015

Int J Syst Assur Eng Manag

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Software test case generation & test oracle design using neural network

Cited by 3 publications

References 11 publications

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

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

The Integration of Machine Learning into Automated Test Generation: A Systematic Literature Review

An approach to design test oracle for aspect oriented software systems using soft computing approach

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