使用人工智能进行软件缺陷的自动分析和报告
Mark Esposito1,2, Saman Sarbazvatan3, Terence Tse1
1Hult International Business School, Cambridge, MA, United States.
Frontiers in artificial intelligence
|December 26, 2024
概括
人工智能 (AI) 通过使用回归测试和自动日志来增强软件开发. 这加快了缺陷检测的速度,提高了应用程序的质量,降低了成本.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
背景情况:
- 随着COVID-19的流行,对高效的商业应用程序的需求加速了.
- 人工智能 (AI) 对于简化运营和提高产品质量至关重要.
研究的目的:
- 确定AI驱动的软件缺陷检测的关键方面.
- 探索AI在增强应用程序生命周期管理中的作用.
主要方法:
- 定性,探索性,描述性和非实验性研究.
- 79个文献参考文献的文献审查.
主要成果:
- 回归测试和自动日志文件在机器学习 (ML) 和机器人过程自动化 (RPA) 中至关重要.
- 这些人工智能技术显著减少了故障识别时间.
结论:
- 人工智能集成使敏捷的软件开发模式成为可能.
- 公司从减少错误,节省成本和提高应用程序质量中受益.
相关概念视频
Quality Assurance
101
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
101
Statistical Analysis System (SAS)
71
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
71
Statgraphics
86
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
86
Statistical Software for Data Analysis and Clinical Trials
279
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
279
Overview of Minitab
64
Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
64
Introduction to R
202
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
202


