The Art and Science of Analyzing Software Data; Quantitative Methods

Tim Menzies, Leandro Minku, Fayola Peters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings - 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, ICSE 2015
PublisherIEEE Computer Society
Pages959-960
Number of pages2
Volume2
ISBN (Electronic)9781479919345
DOIs
Publication statusPublished - 12 Aug 2015
Event37th IEEE/ACM International Conference on Software Engineering, ICSE 2015 - Florence, Italy
Duration: 16 May 201524 May 2015

Conference

Conference37th IEEE/ACM International Conference on Software Engineering, ICSE 2015
Country/TerritoryItaly
CityFlorence
Period16/05/1524/05/15

ASJC Scopus subject areas

  • Software

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