Designing a Metric to Detect Big Balls of Mud

Almost everyone who worked in software development for a while has come in contact with the dreaded big ball of mud (BBoM). If you are not familiar with the term, it describes software systems that have lost their architectural cohesion and suffer from extreme coupling and large cyclic dependency groups. That makes it much harder to do any changes on those systems, because everything is literally connected to everything else. Therefor it requires developers spending almost all their time trying to understand code before they can risk doing changes. And even then, the chance of regression bugs stay pretty high. If a system reaches this state, doing changes becomes so expensive, that rewriting the system from scratch might be cheaper than maintaining the old system. Unfortunately, very often it is not possible to rewrite the system, because the users can’t wait for years for a system replacement. This puts many development organization in a very uncomfortable place.

A big ball of mud visualized by Sonargraph

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Changes to the Sonargraph Dashboard

The Sonargraph dashboard was designed to give the user a quick overview about several important aspects of software quality. The most important aspects in our opinion are “Architecture” and “Structure”, which are displayed at the top of the dashboard. After some internal discussions we decided that we could improve the metrics in the “Structure” box. Those changes will be rolled out with Sonargraph version 15.2.0, which is expected to be released this week.

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Implementing Additional Metrics Using the Sonargraph Script API

With the Sonargraph 9.10 release, we added support for two additional OO-metrics “Depth of Inheritance” and “Number of Children” as described by Chidamber and Kemerer. Sonargraph provides a powerful Script API that allows implementing new metrics as Groovy scripts and I needed surprisingly little amount of code for the implementation. This blog post will explain the scripts’ code and the used Sonargraph Script API in detail.

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A Promising New Metric To Track Maintainability

A good metric to measure software maintainability is the holy grail of software metrics. What we would like to achieve with such a metric is that its values more or less conform with the developers own judgement of the maintainability of their software system. If that would succeed we could track that metric in our nightly builds and use it like the canary in the coal mine. If values deteriorate it is time for a refactoring. We could also use it to compare the health of all the software systems within an organization. And it could help to make decisions about whether it is cheaper to rewrite a piece of software from scratch instead of trying to refactor it.

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