by Saad Shafiq, Atif Mashkoor, Christoph Mayr-Dorn, Alexander Egyed
Abstract:
Context: 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.
Reference:
Machine Learning for Software Engineering: A Systematic Mapping (Saad Shafiq, Atif Mashkoor, Christoph Mayr-Dorn, Alexander Egyed), In CoRR, volume abs/2005.13299, 2020.
Bibtex Entry:
@Article{DBLP:journals/corr/abs-2005-13299,
author = {Saad Shafiq and Atif Mashkoor and Christoph Mayr-Dorn and Alexander Egyed},
journal = {CoRR},
title = {Machine Learning for Software Engineering: {A} Systematic Mapping},
year = {2020},
volume = {abs/2005.13299},
abstract = {Context: 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.},
archiveprefix = {arXiv},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/journals/corr/abs-2005-13299.bib},
eprint = {2005.13299},
file = {:Journals/CORR 2020 - Machine Learning for Software Engineering A Systematic Mapping/Machine Learning for Software Engineering - A Systematic Mapping - preprint.pdf:PDF},
keywords = {SCCH, LIT AI Lab},
timestamp = {Thu, 28 May 2020 17:38:09 +0200},
url = {https://arxiv.org/abs/2005.13299},
}