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

被引:15
|
作者
Shafiq, Saad [1 ]
Mashkoor, Atif [1 ]
Mayr-Dorn, Christoph [1 ]
Egyed, Alexander [1 ]
机构
[1] Johannes Kepler Univ Linz, Inst Software Syst Engn, A-4040 Linz, Austria
基金
奥地利科学基金会;
关键词
Machine learning; Data mining; Tools; Support vector machines; Software testing; Software systems; Software engineering; machine learning; literature review; STATIC CODE METRICS; DEFECT PREDICTION; MODEL; MAINTAINABILITY; RELIABILITY; GENERATION;
D O I
10.1109/ACCESS.2021.3119746
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:140896 / 140920
页数:25
相关论文
共 50 条
  • [1] Machine Learning in Software Development Life Cycle: A Comprehensive Review
    Navaei, Maryam
    Tabrizi, Nasseh
    ENASE: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, 2022, : 344 - 354
  • [2] Impact of Machine Learning on Software Development Life Cycle
    Navaei, Maryam
    Tabrizi, Nasseh
    PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, ENASE 2023, 2023, : 718 - 726
  • [3] Accessibility in the Software Development Life Cycle: A Systematic Literature Review
    Cruz-Portilla, Mauricio
    Carlos Perez-Arriaga, Juan
    Octavio Ocharan-Hernandez, Jorge
    Sanchez-Garcia, Angel J.
    2021 9TH INTERNATIONAL CONFERENCE IN SOFTWARE ENGINEERING RESEARCH AND INNOVATION (CONISOFT 2021), 2021, : 97 - 103
  • [4] Advances in application of machine learning to life cycle assessment: a literature review
    Ghoroghi, Ali
    Rezgui, Yacine
    Petri, Ioan
    Beach, Thomas
    INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT, 2022, 27 (03): : 433 - 456
  • [5] Advances in application of machine learning to life cycle assessment: a literature review
    Ali Ghoroghi
    Yacine Rezgui
    Ioan Petri
    Thomas Beach
    The International Journal of Life Cycle Assessment, 2022, 27 : 433 - 456
  • [6] ZDLC for the Early Stages of the Software Development Life Cycle
    Makoondlall, Y. K.
    Khaddaj, S.
    Makoond, B.
    PROCEEDINGS OF THIRTEENTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE, (DCABES 2014), 2014, : 6 - 12
  • [7] Application of Machine Learning Paradigms for Predicting Quality in Upstream Software Development Life Cycle
    Piyush Mehta
    A. Srividya
    A. K. Verma
    OPSEARCH, 2005, 42 (4) : 332 - 339
  • [8] A review of literature on software used in studies of life cycle
    Campolina, Juliana Mendes
    Sigrist, Carolina Sao Leandro
    da Silva Moris, Virginia Aparecida
    REVISTA ELETRONICA EM GESTAO EDUCACAO E TECNOLOGIA AMBIENTAL, 2015, 19 (02): : 735 - 750
  • [9] A systematic literature review of software effort prediction using machine learning methods
    Ali, Asad
    Gravino, Carmine
    JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS, 2019, 31 (10)
  • [10] Systematic Literature Review on Software Effort Estimation Using Machine Learning Approaches
    Sharma, Pinkashia
    Singh, Jaiteg
    2017 INTERNATIONAL CONFERENCE ON NEXT GENERATION COMPUTING AND INFORMATION SYSTEMS (ICNGCIS), 2017, : 43 - 47