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

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

共 50 条

[11] Systematic literature review of machine learning based software development effort estimation models
Wen, Jianfeng
Li, Shixian
Lin, Zhiyong
Hu, Yong
Huang, Changqin
INFORMATION AND SOFTWARE TECHNOLOGY, 2012, 54 (01) : 41 - 59
[12] A TAXONOMY FOR THE EARLY STAGES OF THE SOFTWARE-DEVELOPMENT LIFE-CYCLE
DAVIS, AM
JOURNAL OF SYSTEMS AND SOFTWARE, 1988, 8 (04) : 297 - 311
[13] Risk management in the software life cycle: A systematic literature review
Masso, Jhon
Pino, Francisco J.
Pardo, Cesar
Garcia, Felix
Piattini, Mario
COMPUTER STANDARDS & INTERFACES, 2020, 71
[14] Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review
Matloob, Faseeha
Aftab, Shabib
Ahmad, Munir
Khan, Muhammad Adnan
Fatima, Areej
Iqbal, Muhammad
Alruwaili, Wesam Mohsen
Elmitwally, Nouh Sabri
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2021, 29 (02): : 403 - 421
[15] Machine/Deep Learning for Software Engineering: A Systematic Literature Review
Wang, Simin
Huang, Liguo
Gao, Amiao
Ge, Jidong
Zhang, Tengfei
Feng, Haitao
Satyarth, Ishna
Li, Ming
Zhang, He
Ng, Vincent
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2023, 49 (03) : 1188 - 1231
[16] Systematic literature review: machine learning for software fault prediction
Navarro Cedeno, Gabriel Omar
Cortes Moya, Katherine
Somarribas Dormond, Ahmed
Gonzalez-Torres, Antonio
Rojas-Hernandez, Yenory
2023 IEEE 41ST CENTRAL AMERICA AND PANAMA CONVENTION, CONCAPAN XLI, 2023, : 134 - 139
[17] Effects of Cognitive-driven Development in the Early Stages of the Software Development Life Cycle
Pinto, Victor Hugo Santiago C.
Oliveira Tavares De Souza, Alberto Luiz
ICEIS: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 2, 2022, : 40 - 51
[18] Systematic Literature Review: Recognition of Human Gait Cycle Using Machine Learning Approach
Kamaruzaman, F. F. A.
Izhar, Che Ani Adi
Fauzilan, A. S.
Setumin, Samsul
Hussain, Z.
Abdullah, M. F.
6TH IEEE INTERNATIONAL CONFERENCE ON RECENT ADVANCES AND INNOVATIONS IN ENGINEERING (ICRAIE), 2021,
[19] Mapping DevOps capabilities to the software life cycle: A systematic literature review
Amaro, Ricardo
Pereira, Rúben
da Silva, Miguel Mira
Information and Software Technology, 2025, 177
[20] A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow
Ahmad, Arshad
Feng, Chong
Khan, Muzammil
Khan, Asif
Ullah, Ayaz
Nazir, Shah
Tahir, Adnan
SECURITY AND COMMUNICATION NETWORKS, 2020, 2020

← 1 2 3 4 5 →