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-theart 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.
Original language | English |
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Title of host publication | Proceedings - 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, ICSE 2015 |
Publisher | IEEE Computer Society |
Pages | 959-960 |
Number of pages | 2 |
Volume | 2 |
ISBN (Electronic) | 9781479919345 |
DOIs | |
Publication status | Published - 12 Aug 2015 |
Event | 37th IEEE/ACM International Conference on Software Engineering, ICSE 2015 - Florence, Italy Duration: 16 May 2015 → 24 May 2015 |
Conference
Conference | 37th IEEE/ACM International Conference on Software Engineering, ICSE 2015 |
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Country/Territory | Italy |
City | Florence |
Period | 16/05/15 → 24/05/15 |
ASJC Scopus subject areas
- Software