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
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;
Conference_Titel :
Software Engineering (ICSE), 2015 IEEE/ACM 37th IEEE International Conference on
Conference_Location :
Florence
DOI :
10.1109/ICSE.2015.306