人工智能自动化测试技术在移动互联网领域的应用研究
Research on Application of Artificial Intelligence Automated Testing Technology in the Field of Mobile Internet
DOI: 10.12677/sea.2024.134053, PDF,   
作者: 王 军:西安交通大学社会智能与复杂数据处理重点实验室,陕西 西安
关键词: 人工智能自动化测试测试效率测试质量测试用例Artificial Intelligence Automated Testing Testing Efficiency Testing Quality Test Case
摘要: 良好质量的软件产品是客户的基本诉求,而良好质量的软件需要更多的测试手段和投入,因此,软件测试是软件研发过程中一个非常重要的环节,会占用大量的项目时间和人力成本,如何高效、及时地完成测试工作一直是软件测试领域的难题之一。随着移动互联网应用行业的迅猛发展,软件的规模和复杂度不断增加,传统的软件测试通过人工手动去设计和执行测试用例,成本高、效率低、局限性大。近年来,人工智能技术在软件测试自动化领域的应用越来越受到关注。本文分析总结了人工智能软件测试自动化技术的优势、应用挑战和未来的发展方向,并提出了一种自动化的测试框架研究实践。人工智能技术在提高软件测试自动化领域的效率和测试质量方面有较大优势,未来的有关研究和应用将会继续深入,推动人工智能技术在软件测试行业的发展。
Abstract: Good quality software products are the basic demands of customers, and good quality software requires more testing methods and investment. Therefore, software testing is a very important part of the software development process, which will occupy a lot of project time and labor costs. How to efficiently and timely complete testing work has always been one of the challenges in the field of software testing. With the rapid development of the mobile Internet application industry, the scale and complexity of software are increasing. Traditional software testing designs and executes test cases manually, which is costly, inefficient and limited. In recent years, the application of artificial intelligence technology in software testing automation has received increasing attention. This article analyzes and summarizes the advantages, application challenges, and future development directions of artificial intelligence software testing automation technology, and proposes an automated testing framework research practice. Artificial intelligence technology has significant advantages in improving the efficiency and quality of software testing automation. Future research and applications will continue to deepen, promoting the development of artificial intelligence technology in the software testing industry.
文章引用:王军. 人工智能自动化测试技术在移动互联网领域的应用研究[J]. 软件工程与应用, 2024, 13(4): 510-515. https://doi.org/10.12677/sea.2024.134053

参考文献

[1] (印度) Tarun Lalwani, 著. QTP自动化测试权威指南[M]. 第2版. 赵旭斌, 阙勇, 韩洪波, 何庆丹, 译. 北京: 人民邮电出版社, 2019.
[2] 赵东明, 张林晓, 张文华. 人工智能背景下软件测试技术应用研究[J]. 信息与电脑(理论版), 2020, 32(23): 132-133.
[3] Castelvecchi, D. (2022) Are ChatGPT and AlphaCode Going to Replace Programmers?
https://www.doc88.com/p-99416185731305.html
[4] 邓正宏, 高逦, 郑玉山. 面向对象自动化测试框架的研究与设计[J]. 微电子学与计算机, 2015, 22(2): 168-171.
[5] 孙伟博, 张斌. 人工智能时代背景下自然语言处理技术的发展[J]. 电子技术与软件工程, 2020(13): 104-105.
[6] 朱少民. 软件测试面临的挑战与发展趋势[J]. 测控技术, 2020, 39(1): 1-4.