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

被引：18

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

来源：

IEEE ACCESS | 2021年 / 9卷

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

页码：140896 / 140920

页数：25

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