Impact of Machine Learning on Software Development Life Cycle

被引：0
作者：
Navaei, Maryam [1 ]
Tabrizi, Nasseh [1 ]
机构：
[1] East Carolina Univ, Dept Comp Sci, East 5th St, Greenville, NC 27858 USA
来源：
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, ENASE 2023 | 2023年
关键词：
Software Engineering; Software Development Life Cycle; Artificial Intelligence; Machine Learning; Machine Learning Algorithms;
D O I：
10.5220/0011997200003464
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
This research concludes an overall summary of the publications so far on the applied Machine Learning (ML) techniques in different phases of Software Development Life Cycle (SDLC) that includes Requirement Analysis, Design, Implementation, Testing, and Maintenance. We have performed a systematic review of the research studies published from 2015-2023 and revealed that Software Requirements Analysis phase has the least number of papers published; in contrast, Software Testing is the phase with the greatest number of papers published.
引用
收藏
页码：718 / 726
页数：9
相关论文
共 58 条
  • [11] Borges O. T., 2021, Challeneges in using Machine Learning to Support Software Engineering-Research Gate
  • [12] Cetiner M., 2020, A Comparative Analysis for Machine Learning based Software Defect Prediction Systems
  • [13] DE-Arteaga M., 2018, Machine Learning for the Developing World
  • [14] Dwivedi A. K., 2016, Applying software metrics for the mining of design pattern
  • [15] Dwivedi A. K., 2016, Software design pattern recognition using machine learning techniques
  • [16] Elhabbash A., 2019, Self-awareness in Software Engineering: A Systematic Literature Review
  • [17] Machine Learning Techniques for Software Maintainability Prediction: Accuracy Analysis
    Elmidaoui, Sara
    Cheikhi, Laila
    Idri, Ali
    Abran, Alain
    [J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2020, 35 (05) : 1147 - 1174
  • [18] England M., 2018, Machine Learning for Mathematical Software
  • [19] Esteves G., 2020, Understanding Machine Learning Software Defect Predictions
  • [20] A software engineering perspective on engineering machine learning systems: State of the art and challenges
    Giray, Gorkem
    [J]. JOURNAL OF SYSTEMS AND SOFTWARE, 2021, 180
123456