Interactive Complexity: Software Metrics from an Ecosystem Perspective

TL;DR

This paper introduces Interactive Complexity, a new way to measure how interconnected software systems are by considering both internal code and external ecosystem interactions, which is crucial for security and reliability.

Contribution

It adapts existing object-oriented metrics to quantify system interactions within software ecosystems, highlighting their correlation with bug fixes.

Findings

Strong correlation between metrics and number of bugs fixed

Metrics effectively capture system interaction complexity

Applicable to large-scale software systems

Abstract

With even the most trivial of applications now being written on top of millions of lines code of libraries, API's, and programming languages, much of the complexity that used to exist when designing software has been abstracted away to allow programmers to focus on primarily business logic. With each application relying so heavily on the ecosystem it was designed to run in, whether that is limited to a local system or includes dependencies on machines connected by networks, measuring the complexity of these systems can no longer be done simply by observing the code internal to the application; we also need to account for its external interactions. This is especially important when considering issues of security, which becomes more vital as our healthcare, financial, and automobiles rely on complicated software systems. We propose Interactive Complexity, which provide a quantitative measure of how intertwined parts of the system are. Some of the most well-known software complexity metrics out there are the metrics in the CK-metric suite; these metrics are designed for use in measuring object oriented systems, but we believe they can be adapted to help measure the interaction of software systems. Our experimental results show strong correlations between the number of bugs fixed in a release and the value of some of these metrics in systems of sufficient scale.