Information Theoretic Objective Function for Genetic Software Clustering

Abstract

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions' limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.

Data clustering: application and trends

“…Some of the direct areas of clustering application generally discussed in the literature have been textual document classification, image segmentation, object recognition, character recognition, information retrieval, data mining, spatial data analysis, business analytics, data reduction, and big data mining. Other areas indicated by Saxena et al ( 2017 ), have been sequence analysis (Durbin et al 1998 ; Li et al 2012 ), human genetic clustering, (Kaplan and Winther 2013 ; Lelieveld et al 2017 ; Marbac et al 2019 ), mobile banking and information system (Motiwalla et al 2019 ; Shiau et al 2019 ), social network analysis (Scott and Carrington 2011 ; Shiau et al 2017 ; Khamparia et al 2020 ), search result grouping (Mehrotra and Kohli 2016 ; Kohli and Mehrotra 2016 ), software evolution (Rathee and Chhabra 2018 ; Izadkhah and Tajgardan 2019 ), recommender systems (Petwal et al 2020 ), educational data mining (Baker 2010 ; Guleria and Sood 2020 ), climatology (Sharghi et al 2018 ; Pike and Lintner 2020 ; Chattopadhyay et al 2020 ) and robotics (Khouja and Booth 1995 ; Zhang et al 2013 ). In Table 6 below we briefly discuss a few applications as indicated by Saxena et al ( 2017 ) and also provide references for more detailed studies.…”

An Enhanced Multifactor Multiobjective Approach for Software Modularization

“…e general structure of the proposed methodology is given in Figure 2. e following is the suggested strategy: First, an object-oriented software MQ, turbo MQ 02 [35] Entropy-based objective function 03 [36] Entropy-based objective function 04 [4] Cohesion and coupling 05 [37] MQ system is used to extract entities (classes) and relationships (direct, indirect, and semantic). Second, assigning weights to different relationships (in this paper, we used weight 1), then aggregating the weights and allocating them to the relationship, shows their strength.…”