How to Identify Risk in a Large Code Base

24th May 2016
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Noah Sussman's profile
Noah Sussman

How to Identify Risk in a Large Code Base image
Talk Description
Identifying risk in software and code is a relatively young science. In 25 years, we’ve moved from using naïve metrics (like bugs-per-line-of-code) to using nuanced models like those applied by automated testing practitioners at Etsy, Google, Facebook and other successful continuous delivery organizations. In these organizations, many people are responsible for understanding and identifying risk in the code base, which is often large and ever-changing in these large applications.

In this Masterclass, Noah demonstrates different techniques and tools he regularly uses to identify and examine risks in large codebases. 

Show Notes
To view the example data and code for the tools mentioned in this talk, you can go to the GitHub links provided and read through the code.
To use the code for the tools mentioned in the talk, you may need to either download the tool or fork the GitHub repo. This will require your own GitHub account (free for personal use) and using the GitHub tutorial on forking repos.
Sample Data
Jupityr/iPython notebook and example data can be found at this GitHub link: https://github.com/textarcana/code-risk/blob/master/code-risk-notebook.ipynb
This notebook allows you to see the results of using each tool described in the talk, so you know what you can expect the outcome to look like when used on your own code base.
Tools Used
Cloc code-repository visualisation tool: http://cloc.sourceforge.net
Scripts Created by Noah for wordcounts, developer contribution, popular files: https://github.com/textarcana/code-risk/tree/master/bin
What you’ll learn

By the end of this masterclass, you'll be able to:

  • Discover the importance of knowing your codebase as a tester
  • Use techniques to identify sources of risk in large codebases
  • Gather evidence to communicate sources of risk in large codebases
Noah Sussman's profile'

Noah Sussman

Noah Sussman is an industrial scientist who studies how people and computers relate to each other. After a decade of developing eCommerce interactive experiences, Noah grew increasingly interested in approaches to scaling Web applications. In particular his approach to scaling CI systems has a history of repeated success.

He is also noted for designing innovative test architectures for The SAT Test, Etsy and Barnes & Noble. He works at Teachers Pay Teachers: the world's largest educational marketplace, where he continues to push the envelope on continuous deployment praxis and tools.

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Tags

  • analysis
  • risks
  • testing-tools