Acquisition of Open Source Software Project Maturity Based on Time Series Machine Learning

Liu, Qin; Li, Xiaolong; Zhu, Hongming; Fan, Hongfei

doi:10.1109/iscid.2017.20

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…We encourage researchers to investigate how ML can be used to automate certain tasks in this area. We further encourage researchers to adopt combinations of ML techniques and use diverse datasets from different sources in order to train the ML models so that the applicability of the techniques can be generalized as also observed in [116,131,190,197].…”

Section: Discussionmentioning

confidence: 99%

“…Future directions include improvement of precision while maintaining recall in ML models [70]. Researchers also emphasized on improving prediction accuracy of the ML model by conducting more experiments using larger numbers of datasets and software applications [116,131,190,197]. Furthermore, evaluation of similar studies with alternate ML techniques are suggested by researchers, which can further strengthen the knowledge base in terms of prediction capability [11,48,73,186].…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning for Software Engineering: A Systematic Mapping

Shafiq,

Mashkoor,

Mayr-Dorn

et al. 2020

Preprint

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The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for improving the software engineering life cycle itself is yet to be discovered, i.e., up to what extent machine learning can help reducing the effort/complexity of software engineering and improving the quality of resulting software systems. To date, no comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering.Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study. Conclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning for Software Engineering: A Systematic Mapping

Shafiq,

Mashkoor,

Mayr-Dorn

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: B Relation Of Sdlc Stages With MLmentioning

confidence: 99%

“…In order to improve prediction accuracy and better reliability of results, more experiments using larger numbers of datasets and software applications have also been suggested [99,115,188,237].…”

Section: Future Research Directionsmentioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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A Systematic Literature Review of Machine Learning Applications in Software Engineering

Mezouar

Afia

2022

Lecture Notes in Networks and Systems

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Acquisition of Open Source Software Project Maturity Based on Time Series Machine Learning

Cited by 3 publications

References 5 publications

Machine Learning for Software Engineering: A Systematic Mapping

Machine Learning for Software Engineering: A Systematic Mapping

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

A Systematic Literature Review of Machine Learning Applications in Software Engineering

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