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Shinobi: A Novel Approach for Context-Driven Testing (CDT) Using Heuristics and Machine Learning for Web Applications

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Industrial Networks and Intelligent Systems (INISCOM 2018)

Abstract

Context-Driven Testing is widely used in the Agile World. It optimizes the testing value and provides an effective way to detect unexpected bugs. Context-driven testing requires the testing team to leverage the full knowledge and skills to solve the problem or to make a decision. In this paper, we propose an approach for Context-Driven Testing using Heuristics and Machine Learning for web applications with a framework called Shinobi. The framework can detect web controls, suggest a set of heuristic values, recognize the meaningful input data, and detect changes of application to recommend test ideas. In the context of improvising the testing performance, Shinobi is considered as Test Assistant for context-driven testers. Shinobi is a PoC to prove the idea of using Machine Learning to develop a Virtual Tester to improve the test quality and train junior testers as responsible testers. The framework is well integrated into all eCommerce projects at MeU Solutions which is a value-added advantage for testing.

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Author information

Authors and Affiliations

  1. Duy Tan University, Da Nang, 550000, Vietnam

    Duc-Man Nguyen & Nhu-Hang Ha

  2. MeU Solutions, Ho Chi Minh City, Vietnam

    Hoang-Nhat Do & Dinh-Thien Vo

  3. Ha Noi University of Science and Technology, Ha Noi, Vietnam

    Quyet-Thang Huynh

Authors
  1. Duc-Man Nguyen
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  2. Hoang-Nhat Do
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  3. Quyet-Thang Huynh
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  4. Dinh-Thien Vo
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  5. Nhu-Hang Ha
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Correspondence to Duc-Man Nguyen .

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Editors and Affiliations

  1. Queen’s University Belfast, Belfast, UK

    Trung Q Duong

  2. Duy Tan University, Da Nang, Vietnam

    Nguyen-Son Vo

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Nguyen, DM., Do, HN., Huynh, QT., Vo, DT., Ha, NH. (2019). Shinobi: A Novel Approach for Context-Driven Testing (CDT) Using Heuristics and Machine Learning for Web Applications. In: Duong, T., Vo, NS. (eds) Industrial Networks and Intelligent Systems. INISCOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 257. Springer, Cham. https://doi.org/10.1007/978-3-030-05873-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-05873-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05872-2

  • Online ISBN: 978-3-030-05873-9

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