Software quality prediction: An investigation based on machine learning

Sandeep Reddivari; Jayalakshmi Raman

doi:10.1109/IRI.2019.00030

Software quality prediction: An investigation based on machine learning

Sandeep Reddivari, Jayalakshmi Raman

School of Computing

University of North Florida

Research output: Chapter or Contribution to Book › Literary contribution

Original language	English
Title of host publication	Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019
Pages	115-122
Number of pages	8
ISBN (Electronic)	9781728113371
DOIs	https://doi.org/10.1109/IRI.2019.00030
State	Published - Jul 2019
Event	20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019 - Los Angeles, United States Duration: Jul 30 2019 → Aug 1 2019

Publication series

Name	Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019

Conference

Conference	20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019
Country/Territory	United States
City	Los Angeles
Period	7/30/19 → 8/1/19

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Science Applications
Human-Computer Interaction
Information Systems

Keywords

Defect prediction
Machine learning
Maintainability prediction
Software maintainability
Software quality
Software reliability

Access to Document

10.1109/IRI.2019.00030

Cite this

Reddivari, S., & Raman, J. (2019). Software quality prediction: An investigation based on machine learning. In Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019 (pp. 115-122). Article 8843447 (Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019). https://doi.org/10.1109/IRI.2019.00030

Software quality prediction: An investigation based on machine learning. / Reddivari, Sandeep; Raman, Jayalakshmi.
Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019. 2019. p. 115-122 8843447 (Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019).

Research output: Chapter or Contribution to Book › Literary contribution

Reddivari, S & Raman, J 2019, Software quality prediction: An investigation based on machine learning. in Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019., 8843447, Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019, pp. 115-122, 20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019, Los Angeles, United States, 7/30/19. https://doi.org/10.1109/IRI.2019.00030

Reddivari S, Raman J. Software quality prediction: An investigation based on machine learning. In Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019. 2019. p. 115-122. 8843447. (Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019). doi: 10.1109/IRI.2019.00030

Reddivari, Sandeep ; Raman, Jayalakshmi. / Software quality prediction : An investigation based on machine learning. Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019. 2019. pp. 115-122 (Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019).

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title = "Software quality prediction: An investigation based on machine learning",

keywords = "Defect prediction, Machine learning, Maintainability prediction, Software maintainability, Software quality, Software reliability",

author = "Sandeep Reddivari and Jayalakshmi Raman",

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Software quality prediction: An investigation based on machine learning

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this