How successful is my (open use) scientific software?

Slides: http://bit.ly/mozfestsoftwaresuccess

Notes: http://bit.ly/mozfestsoftwaresuccessnotes

Who we are

Neil Chue Hong, Software Sustainability Institute

Catherine Jones, Software Engineering Support Centre, STFC

Andrew Nesbitt, Libraries.io

Get these slides

http://bit.ly/mozfestsoftwaresuccess

Measuring software is hard

What are we measuring?

quality

engagement

impact

Why are we measuring it?

improvement

comparison

Introductions

Introductions

Your name

Where you're from

One thing you want to learn from this workshop

Identifying Successful Software

Pair Activity

1. Break into pairs
2. One person in the pair:
• Chooses a piece of research software they think is successful
• Describe to your partner what measures make you think it is successful
• Your partner writes these on a Post-It note as you talk
3. Swap and do the same for the other person in the pair
4. Discuss whether you had the same ways of identifying successful software, and where they differed.

How Do People Identify Success?

Discussion

What was important?

What was unexpected?

What's easier to measure?

What's more accurate?

Does the opposite hold?

Measuring Software

Group Exercise

1. Pick three measures from those suggested that are important to your group
2. Create a set of metrics that would allow you to rate a piece of software against that measure.
3. Depending on whether we have internet access:
   • Apply your metrics to a piece of software one of your group is familiar with, OR
   • Use these metrics to rank the following set of software:
     • scipy: https://github.com/scipy/scipy
     • Spyder: https://github.com/spyder-ide/spyder
     • pycse: https://github.com/jkitchen/pycse

What are existing models of success?

Many other schemes

See our reference list: https://github.com/softwaresaved/MozFest2016-softwaresuccess/blob/master/references.md

Contribute via pull requests

Depsy

Depsy: scipy

Depsy: Spyder

Depsy: pycse

Libraries.io and SourceRank

Libraries.io: scipy

SourceRank: scipy

Libraries.io: Spyder

Libraries.io: pycse

What we've learned

What we've learned

Many different ways to measure software

Different people will place different importance
on different measures

Some things are easy to measure,
some things are useful to measure

There are already examples out there -
some may fit your requirements

Contribute your own ideas!