Using Machine Learning to Measure and Manage Technical Debt

3 years ago thenewstack.io

Summary: This is a summary of an article originally published by The New Stack. Read the full original article here →

https://www.linkedin.com/in/ori-saporta-9023b43/?originalSubdomain=il'' If you’re a software developer, then “technical debt” is probably a term you’re familiar with. Technical debt, in plain words, is an accumulation over time of lots of little compromises that hamper your coding efforts.

We took a similar approach and devised a method to measure technical debt of an application based on the dependency graph between its classes.

The hypothesis here is that by using these generic metrics on the dependency graphs, we can identify architectural issues that represent real technical debt in the original code base.

This is intended to help organizations build a business case for handling and removing architectural technical debt from their applications.

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