On Computing the Canonical Features of Software Systems

Abstract

Software applications typically have many features that vary in their similarity. We define a measurement of similarity between pairs of features based on their underlying implementations and use this measurement to compute a set of canonical features. The Canonical Features Set (CFS) consists of a small number of features that are as dissimilar as possible to each other, yet are most representative of the features that are not in the CFS. The members of the CFS are distinguishing features and understanding their implementation provides the engineer with an overview of the system undergoing scrutiny. The members of the CFS can also be used as cluster centroids to partition the entire set of features. Partitioning the set of features can simplify the understanding of large and complex software systems. Additionally, when a specific feature must undergo maintenance, it is helpful to know which features are most closely related to it. We demonstrate the utility of our method through the analysis of the Jext, Firefox, and Gaim software systems.

Using structural and textual information to capture feature coupling in object-oriented software

“…Similarly, Giroux and Robillard (Giroux and Robillard 2006) defined a measure for feature coupling across versions of a system using regression tests since tests typically align with features. The association graph matching similarity measure (AGM) introduced by Kothari et al (Kothari et al 2006) is a measure of pair-wise similarity between features based on dynamic call graphs. It has been used to find canonical feature sets (Kothari et al 2006), feature version similarity (Kothari et al 2008), and feature implementation overlap (Kothari et al 2007).…”

Using compression algorithms to support the comprehension of program traces

“…Eisenbarth et al [4] have attempted to combine dynamic analysis with static analysis and Formal concept analysis to identify potential features. More recently, Greevy and Ducasse [5], Kothari et al [6] and Wanatabe et al [14] have all worked on the identification of trace phases to identify features in the source code.…”

Resolving feature dependency implementations inconsistencies during product derivation

“…Kothari et al [21] proposed an approach to system comprehension that considers features as the primary unit of analysis. The work provides a mechanism to define a relationship between features based on comparing the feature implementation.…”