Software Evolution anno 2014:directions and challenges

Alexander Serebrenik

@aserebrenik

a.serebrenik@tue.nl

2008

Time for a new book!

2014

2008 vs. 2014

From systems to ecosystems

Business-oriented view

“a set of actors functioning as a unit and interacting with a shared market for software and services, together with the relationships among them.”

with thanks to International Data Corporation (IDC)

Development-centric view

a collection of software projects that are developed and evolve together in the same environment

with thanks to Bram Adams

Socio-technical viewa community of persons (end-users, developers, debuggers, …) contributing to a collection of projects

Technical

Scientific

Practical

Legal and ethical

Technical challenges

• eliminate non-names• eliminate specific quirks• group “similar” names

– first/last name – textual similarity– latent semantic analysis

• (correct groups manually)

Technical challenges

Technical challenges

• eliminate non-names• eliminate specific quirks• group “similar” names

– first/last name – textual similarity– latent semantic analysis

• (correct groups manually)

Technical challenges

Structured data2008

Unstructured data2014

Technical challenges

Structured data2008

Unstructured data2014

Scientific challenges

Scientific challenges

Raw dataProcessed data set

Tools & scripts

#MSR papers 2004-2009

Y Y Y 2Y Y N 2Y P Y 1Y P P 2Y P N 2Y N Y 16Y N P 19Y N N 64P N Y 1P N N 2N Y N 2N P N 1N N Y 7N N P 2N N N 31N/A N/A N/A 17

We share raw data but rarely share tools – reinventing the wheel anybody?

Practical challenges

• How can we share our big data with other researchers?• Different formats, different tools, storage

problems, …• How can we make our research results useful

to practitioners and development communities?

• How can we build tools and dashboards that integrate our findings?

Legal and ethical challenges

(especially for survey data)

http://www.intracto.com/blog/online-privacy-belangrijk

k-anonymity

k-anonymity

l-diversityt-closeness

2008 vs. 2014

From “traditional” to “non-traditional” artifacts:

What is software?

http://ctms.engin.umich.edu/CTMS/index.php?example=Introduction&section=SimulinkModeling

Maintainability???Evolution???

BumbleBee: a refactoring tool for spreadsheets

with thanks to Felienne Hermans

http://help.eclipse.org/juno/index.jsp?topic=%2Forg.eclipse.m2m.atl.doc%2Fguide%2Fconcepts%2FModel-Transformation.html

http://help.eclipse.org/juno/index.jsp?topic=%2Forg.eclipse.m2m.atl.doc%2Fguide%2Fconcepts%2FModel-Transformation.html

• describe evolutionary steps • relate to changes of other

artifacts• describe prevalence in

practice • support automation

New kind of verification

artifacts

2008

2009

2012

2013

2008 vs. 2014

From technical to socio-technical perspective:

Who are these people?

What do they do?

> 90% in WordPress & Drupal> 95% in FLOSS surveys> 87% in GNOME> 70% in software-related jobs (NSF)

MEN

FLOSS 2013

Europe,US,CA,AUBrazil/Argentina

How can we reliably and efficiently identify gender, age, location?

Technical challenges

?

Name + Location = Gender

Lonzo Alonzo ⇒

w35l3y wesley ⇒

Name + Location = Gender

<title>Ben Kamens</title>…<h1>We&#8217;re willing to be embarrassed about what we <em>haven&#8217;t</em> done&#8230;</h1>

Heuristics: title + first h1

Ben Kamens We’re willing to be embarrassed about what we haven’t done…

<PERSON>Ben Kamens</PERSON> We’re willing to be embarrassed about what we haven’t done…

Stanford Named Entity Tagger

Quality of gender resolution: SurveySelf-identification

As inferred TotalM F ?

M 60 3 43 106F 2 5 4 11

Self-identification

As inferred TotalM F ?

M 90 3 13 106F 2 9 0 11

+ avatars, other social media sites (manually)

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Occasional contributors

Frequent contributors

How can we reliably and efficiently identify human activities?

Technical challenges

How can we reliably and efficiently identify human activities?

Technical challenges