Investigating the use of lexical information for software system clustering

Abstract

Developers have a lot of freedom in writing comments as well as in choosing identifiers and method names. These are intentional in nature and provide a different relevance of information to understand what a software system implements, and in particular the role of each source file.In this paper we investigate the effectiveness of exploiting lexical information for software system clustering. In particular we explore the contribution of the combined use of six different dictionaries, corresponding to the six parts of the source code where programmers introduce lexical information, namely: class, attribute, method and parameter names, comments, and source code statements. Their relevance has been weighted by means of a probabilistic model, whose parameters have been estimated by the Expectation-Maximization algorithm. To group source files accordingly we used a hierarchical clustering algorithm. The investigation has been conducted on a dataset of 13 open source Java software systems.

A comparative analysis of software architecture recovery techniques

“…• Algorithm for Comprehension-Driven Clustering (ACDC) [47] [14], [15] and two of its variants, • Architecture Recovery using Concerns (ARC) [21]. The selected techniques have been previously published, they are automated, and they have been designed specifically for architecture recovery.…”

“…Recent work has focused on utilizing textual input [14], [15], [33], [21] obtained from source code and comments. The resulting techniques combine textual input with other commonly-used architecture recovery mechanisms, such as objective function maximization or hierarchical clustering.…”

“…For our comparative analysis, we have considered both variants of Bunch-NAHC and SAHC-to obtain the most accurate clustering that this technique can provide. ZBR, or zone-based recovery, is a technique developed by Corrazza et al [14], [15], which utilizes textual information, hierarchical clustering, and a weighting scheme for feature vectors. We have selected the latest formulation of Corazza et al's technique [15].…”

“…ZBR, or zone-based recovery, is a technique developed by Corrazza et al [14], [15], which utilizes textual information, hierarchical clustering, and a weighting scheme for feature vectors. We have selected the latest formulation of Corazza et al's technique [15]. The textual information used in ZBR tries to infer a software system's semantics when recovering the system's architecture.…”

“…To better understand the accuracy of existing architecture recovery techniques and to address the shortcomings encountered in previous comparative studies, this paper presents a comparative analysis of six automated architecture recovery techniques: Algorithm for Comprehension-Driven Clustering (ACDC) [47], Architecture Recovery using Concerns (ARC) [21], Bunch [34], scaLable InforMation BOttleneck (LIMBO) [6], Weighted Combined Algorithm (WCA) [27], and a technique developed by Corazza et al [15], which we will refer to as zone-based recovery (ZBR) in this paper. We have selected these techniques because they have been shown to be accurate based on prior work and have been evaluated in several previous publications.…”