DocumentCode
1638697
Title
The Art and Science of Analyzing Software Data; Quantitative Methods
Author
Menzies, Tim ; Minku, Leandro ; Peters, Fayola
Author_Institution
Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
Volume
2
fYear
2015
Firstpage
959
Lastpage
960
Abstract
Using the tools of quantitative data science, software engineers that can predict useful information on new projects based on past projects. This tutorial reflects on the state-of-the-art in quantitative reasoning in this important field. This tutorial discusses the following: (a) when local data is scarce, we show how to adapt data from other organizations to local problems; (b) when working with data of dubious quality, we show how to prune spurious information; (c) when data or models seem too complex, we show how to simplify data mining results; (d) when the world changes, and old models need to be updated, we show how to handle those updates; (e) when the effect is too complex for one model, we show to how reason over ensembles.
Keywords
data analysis; data mining; software quality; data mining; quantitative data science tools; quantitative methods; software data analysis; software engineers; Art; Computer science; Data mining; Data models; Software; Software engineering; Tutorials;
fLanguage
English
Publisher
ieee
Conference_Titel
Software Engineering (ICSE), 2015 IEEE/ACM 37th IEEE International Conference on
Conference_Location
Florence
Type
conf
DOI
10.1109/ICSE.2015.306
Filename
7203128
Link To Document