Measuring And Improving Software Development Productivity We apply machine learning to version control data to measure software development productivity. our models measure both the quantity and quality of produced code. Measuring software development productivity: a machine learning approach. jean helie, ian wright, albert ziegler. paper presented at the 'machine learning for programming' workshop.
Measuring Software Development Productivity Swarmia Measuring software development productivity: a machine learning approach. j. helie, i. wright, a. ziegler. paper presented at the ‘machine learning for programming’ workshop. We apply machine learning to version control data to measure software development productivity. our models measure both the quantity and quality of produced code. 16:45 ian wright, jean helie and albert ziegler measuring software development productivity: a machine learning approach speaker: ian wright abstract. we apply machine learning to version control data to measure software development productivity. our models measure both the quantity and quality of produced code. This document discusses measuring software development productivity using machine learning models trained on version control data. it proposes measuring both the quantity and quality of code produced.
Measuring Software Development Productivity Swarmia 16:45 ian wright, jean helie and albert ziegler measuring software development productivity: a machine learning approach speaker: ian wright abstract. we apply machine learning to version control data to measure software development productivity. our models measure both the quantity and quality of produced code. This document discusses measuring software development productivity using machine learning models trained on version control data. it proposes measuring both the quantity and quality of code produced. Dat offers a precise, yet high coverage measure for development productivity, aiding business decisions. it enhances development efficiency by aligning the internal development workflow with the experiment driven culture of external product development. To use a sufficiently nuanced system of measuring developer productivity, it’s essential to understand the three types of metrics that need to be tracked: those at the system level, the team level, and the individual level. Measuring software development productivity: a machine learning approach. ian wright, jean helie and albert ziegler. If we can measure software development productivity we will know if changes we make to the people (e.g., individuals, roles, responsibilities), processes (e.g., scrum, kanban), or technology (e.g., language, ide) are improving productivity or not.