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An Intelligent Journey to Machine Learning Applications in Component-Based Software Engineering

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Advances in Computing and Intelligent Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

The automation of software development process is a leading edge for the Software 2.0 trend. Machine learning with software engineering has been used in a variety of domains and in all the phases of the software development life cycle process. The journey of machine learning in software engineering lays down the time lines and milestones to be achieved in the intelligent automation process of software development. From designing to testing and security, machine learning has automated almost all the phases of software development life cycle with supervised and unsupervised learning and the future holds the panoramic view of automated intelligence where the machine learns by itself without any explicit programming. The goal of the paper is to provide useful insight into the significant arena of software intelligence andlay down the potential ground for various research analysis in the software engineering processes.

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Author information

Authors and Affiliations

  1. CSE Department, Lingayas VidyaPeeth, Faridabad, Haryana, India

    Divanshi Priyadarshni Wangoo

Authors
  1. Divanshi Priyadarshni Wangoo
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Correspondence to Divanshi Priyadarshni Wangoo .

Editor information

Editors and Affiliations

  1. Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. University of Southern Denmark, Odense, Denmark

    Kannan Govindan

  3. Amity University, Jaipur, Rajasthan, India

    Ramesh C. Poonia

  4. Amity University, Jaipur, Rajasthan, India

    Sandeep Kumar

  5. University of Bahrain, Zallaq, Bahrain

    Wael M. El-Medany

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Wangoo, D.P. (2020). An Intelligent Journey to Machine Learning Applications in Component-Based Software Engineering. In: Sharma, H., Govindan, K., Poonia, R., Kumar, S., El-Medany, W. (eds) Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0222-4_16

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