@Article{Shafiq2021,
  author    = {Saad Shafiq and Atif Mashkoor and Christoph Mayr-Dorn and Alexander Egyed},
  journal   = {{IEEE} Access},
  title     = {A Literature Review of Using Machine Learning in Software Development Life Cycle Stages},
  year      = {2021},
  pages     = {140896--140920},
  volume    = {9},
  abstract  = {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.},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl    = {https://dblp.org/rec/journals/access/ShafiqMME21.bib},
  doi       = {10.1109/ACCESS.2021.3119746},
  file      = {:Journals/IEEE 2021 - A Literature Review of Using Machine Learning in Software Development Life Cycle Stages/A Literature Review of Using Machine Learning in Software Development Life Cycle Stages - preprint.pdf:PDF},
  timestamp = {Wed, 03 Nov 2021 08:25:34 +0100},
  url       = {https://doi.org/10.1109/ACCESS.2021.3119746},
}