Software analytics, the next thing for software metrics in modern companies

The hot summer in Europe provided a lot of time for relaxation and contemplation:) I’ve spent some of the warm days reading some articles for the upcoming SEAA session on software analytics, which is a follow up of the special issue of IST: 

Software analytics, simply put, is using data and its visualisation to make decisions about software development. The typical data sources, both in literature and observed in many companies, are:

  1. Source code measurements from Git
  2. Defect data from JIRA
  3. Requirements data
  4. Customer data, a.k.a. field data
  5. Performance/profiling data from running the system
  6. Process data from time reporting systems, Windows journals, etc.

These data sources allow us to find bottlenecks in the performance of our software and the performance of our progress.

Software analytics has been in the heart of such paradigms as the MVP from The Lean Start-Up, where they provide the ability to steer which features are developed and which are abandoned.

Our experiences from Software Analytics are described in the book Software Development Measurement Programs, chapter 5: 


Author: Miroslaw Staron

I’m professor in Software Engineering at IT faculty. I usually blog about interesting articles (for me) and my own reflections on the development of Software Engineering, AI, computer science and automotive software.

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