Using Network Analysis for Recommendation of Central Software Classes

As a new developer, getting to know a large unknown software system is a challenging task. If experienced developers are available, they can suggest which classes to read first, helping new developers to quickly grasp the system´s most fundamental concepts. In practice, however, experienced developers often are no longer available. In these cases, the set of most important classes must be reverse engineered. This paper presents a thorough analysis of using different network analysis metrics on dependency graphs to retrieve central classes. An empirical study on four open source projects evaluates the results based on a survey among the systems´ core developers. It demonstrates that the algorithmic results can compete with the suggestions of experienced developers.