2013 07 05 (uc3m) lasi emadrid grobles jgbarahona urjc lecciones aprendidas analitica software...

About MSRTypes of analysis

Possible lessons to be learned

10+ years of software analyticsLessons learned that may be useful for learning analytics

Gregorio Robles, Jesus M. Gonzalez Barahona

{grex,jgb}@gsyc.urjc.esGSyC/LibreSoft, Universidad Rey Juan Carlos, Madrid, Spain

LASI, July 5th, 2013

Gregorio Robles, Jesus M. Gonzalez Barahona 10+ years of software analytics



c©2013 Gregorio Robles, Jesus M. Gonzalez-Barahona

All figures are ours, except when the original source is specified.

Some rights reserved. This presentation is distributed under the“Attribution-ShareAlike 3.0” license, by Creative Commons, available at

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http://creativecommons.org/licenses/by-sa/3.0/



What is this talk about?




About us

What we do

Main goal: Understandingfree software

Main focus: empiricalsoftware engineering

Main method: retrieval ofpublicly available data

Part of the GSyC/LibreSoftresearch team

Spin-off company: Bitergia

The other author

Jesus M. Gonzalez-BarahonaMember of eMadrid

Universidad Rey Juan CarlosMadrid, Spain




Table of Contents

1 About MSR

2 Types of analysis

3 Possible lessons to be learned




Data in software development




Mining Software Repositories

Figure: Mining Software Repositories: http://msrconf.org




Public available data sources

Mainly from free software projects

Artifacts

Mainly source code

Repositories (with meta-data)

Versioning systemsBug-tracking systemMailing list, forums, etc.Other: twitter, chats, etc.




Artifacts vs. repositories

Figure: by Israel HerraizGregorio Robles, Jesus M. Gonzalez Barahona 10+ years of software analytics



Table of Contents

1 About MSR

2 Types of analysis





Important keywords




Recommender systems

Figure: Ahmed Lamkanfi et al., MSR 2010




Social Network Analysis

Figure: Interactions for Linux 1.0.




Developer territoriality (the toothbrush effect)

Figure: Daniel German, 2004Gregorio Robles, Jesus M. Gonzalez Barahona 10+ years of software analytics



Entry patterns




Table of Contents

1 About MSR

2 Types of analysis





Correlation and causation




Not the right data

Figure: Sometimes we have lots of data... but not the one required toanswer the question! Picture: (c) StatusMind.com




Noise!

Figure: Tools have been thought for learning, not for analytics! Pic. (c)www.socialresearchmethods.net




Diversity

Figure: There is not size-fits-all. Every project has its own processes,peculiarities, history and culture.




Challenges for the research community

Figure: Challenges not only on existing data sources (it is not only aboutthe method!). Allow having new data sources as a challenge per se.Picture: (c) Redeem the commute




Repositories

Figure: There is a need for the existence of public repositories where datais shared. There is always a data source that you did not think about!Links among data in the sources is difficult to gather.




Replication

Figure: Have you thought about replicating your studies later? Andhaving it replicated by an independent resarch group? Figure: (c) 2009Archangel




To take away!

We’ve shown the experience of analytics in another field

There are plenty of ideas, concepts, models and algorithmsthat can be used

We may have lots of data to analyze... but

Be aware that correlation is not causation!We need good (and the right) dataWe need diverse dataWe need to think about replicability




10+ years of software analyticsLessons learned that may be useful for learning analytics

Gregorio Robles, Jesus M. Gonzalez Barahona

{grex,jgb}@gsyc.urjc.esGSyC/LibreSoft, Universidad Rey Juan Carlos, Madrid, Spain

LASI, July 5th, 2013


2013 07 05 (uc3m) lasi emadrid grobles jgbarahona urjc lecciones aprendidas analitica software...

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