Mining Software Effort Data: Preliminary Analysis of Visual Studio Team System Data

Lucas Layman, Nachiappan Nagappan, Sam Guckenheimer, Jeff Beehler, and Andrew Begel

Abstract

In the software development process, scheduling and predictability are important components to delivering a product on time and within budget. Effort estimation artifacts offer a rich data set for improving scheduling accuracy and understanding the develop-ment process. Effort estimation data for 55 features in the latest release of Visual Studio Team System (VSTS) were collected and analyzed for trends, patterns, and differences. Statistical analysis shows that actual estimation error was positively correlated with feature size, and that in-process metrics of estimation error were also correlated with the final estimation error. These findings suggest that smaller features can be estimated more accurately, and that in-process estimation error metrics can be provide a quantitative supplement to developer intuition regarding high-risk features during the development process.

Details

Publication typeInproceedings
Published inProceedings of the 2008 International Working Conference on Mining Software Repositories
URLhttp://doi.acm.org/10.1145/1370750.1370762
Pages43–46
ISBN978-1-60558-024-1
AddressNew York, NY, USA
PublisherACM
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