Automatic identification of key classes in a software system using webmining techniques

Abstract

Software engineers new to a project are often stuck sorting through hundreds of classes in order to find those few classes that offer a significant insight into the inner workings of the software project. To help stimulate this process, we propose a technique that can identify the most important classes in a system or the key classes of that system. Software engineers can use these classes to focus their understanding efforts when starting to work on a new software project. Those key classes are typically characterized with having a lot of ‘control’ within the application. In order to find these controlling classes, we present a detection approach that is based on dynamic coupling and webmining. We demonstrate the potential of our technique using two open‐source software systems that have a rich documentation set. During the case studies we use dynamically gathered coupling information that vary between a number of coupling metrics. The case studies show that we are able to retrieve 90% of the classes deemed important by the original maintainers of the systems, while maintaining a level of precision of around 50%. Copyright © 2008 John Wiley & Sons, Ltd.

Automatic identification of class stereotypes

“…A few approaches identify key or most important classes in a software system [17], [18]. Zaidman et al [17] provide a mechanism based on dynamic coupling and webmining to find classes with a lot of "control" within the application.…”

“…Zaidman et al [17] provide a mechanism based on dynamic coupling and webmining to find classes with a lot of "control" within the application. Orla Greevy et al [18] identify the key classes and methods which provide functionality for individual features.…”

Towards Understanding the Behavior of Classes Using Probabilistic Models of Program Inputs

“…For software comprehension, the idea of landmarks was also used. Indeed in [11] and [13], key classes are identified to serve as starting points for program comprehension.…”

“…The identification of comprehension starting points is often based on coupling [11,13]. The rationale behind this decision is that elements that are tightly coupled to other elements are likely to implement the most important concepts of a program.…”

Using Data Fusion and Web Mining to Support Feature Location in Software

“…Textual information is analyzed by IR, execution information is collected by dynamic analysis, and dependencies are analyzed using web mining. Applying web mining to feature location is a novel idea, but it has been previously used for other program comprehension tasks, such as identifying key classes for program comprehension [32], ranking components in a software repository [19], and recommending related API calls [29]. Software lends itself well to web mining approaches, because like the World Wide Web, software can be represented by a graph, and that graph can be mined for useful information such as the source code that implements a feature.…”