.png){kind=icon}
.png){kind=icon}
Return
How does Machine Learning Change Software Development Practices?
.png){kind=icon}
OA
AI
DOI：10.1109/TSE.2019.2937083.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). Based on our findings, we highlight future research directions and provide recommendations for practitioners.
Keywords:
Software
Interviews
Data models
Machine learning
Testing
Task analysis
Software engineering
Software engineering
machine learning
practitioner
empirical study
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.6
Papers:
2.8K
Citations:
1.1W
Organization
View more organizations