Information-theoretic software clustering

Andritsos, Periklis; Tzerpos, Vassilios

doi:10.1109/tse.2005.25

Cited by 209 publications

(141 citation statements)

References 24 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…Another set of metrics has come out of informationtheoretic approaches to software clustering [30], [31]. Here, the goal is to characterize modules on the basis of the predictive power (as measured by, say, mutual information in the information-theoretic sense) of the software artifacts vis-a-vis the modules in which the artifacts reside.…”

Section: Previous Work On Software Metrics Relevant To Our Contributionmentioning

confidence: 99%

API-Based and Information-Theoretic Metrics for Measuring the Quality of Software Modularization

Sarkar

RAMA

Kak

2007

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Abstract-We present in this paper a new set of metrics that measure the quality of modularization of a non-object-oriented software system. We have proposed a set of design principles to capture the notion of modularity and defined metrics centered around these principles. These metrics characterize the software from a variety of perspectives: structural, architectural, and notions such as the similarity of purpose and commonality of goals. (By structural, we are referring to intermodule coupling-based notions, and by architectural, we mean the horizontal layering of modules in large software systems.) We employ the notion of API (Application Programming Interface) as the basis for our structural metrics. The rest of the metrics we present are in support of those that are based on API. Some of the important support metrics include those that characterize each module on the basis of the similarity of purpose of the services offered by the module. These metrics are based on information-theoretic principles. We tested our metrics on some popular open-source systems and some large legacy-code business applications. To validate the metrics, we compared the results obtained on human-modularized versions of the software (as created by the developers of the software) with those obtained on randomized versions of the code. For randomized versions, the assignment of the individual functions to modules was randomized.

show abstract

Section: Previous Work On Software Metrics Relevant To Our Contributionmentioning

confidence: 99%

API-Based and Information-Theoretic Metrics for Measuring the Quality of Software Modularization

Sarkar

RAMA

Kak

2007

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…We have applied the MULICsoft algorithm to cluster the Mozilla software system. The process of extracting static and dynamic information for Mozilla was presented in [3]. There are 1202 objects in the Mozilla data set, corresponding to 1202 source files of the Mozilla system.…”

Section: Description Of Data Setsmentioning

confidence: 99%

“…Several clustering algorithms for software have been presented in the literature [8,9,3]. Some of the previous software clustering tools can consider the dynamic information (i.e., the number of function calls during the run time) in the clustering process [12,3] but most can not.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the previous software clustering tools can consider the dynamic information (i.e., the number of function calls during the run time) in the clustering process [12,3] but most can not. In this section, we describe three established clustering algorithms: BUNCH, ACDC and LIMBO.…”

Section: Related Workmentioning

confidence: 99%

“…LIMBO has been successfully applied to the software clustering problem [3]. LIMBO's goal is to create clusters whose features contain as much information as possible about the features of their contents.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Clustering large software systems at multiple layers

Andreopoulos

Tzerpos

et al. 2007

Information and Software Technology

View full text Add to dashboard Cite

show abstract

Using reverse engineering techniques to infer a system use case model

Miranda

Berón

Montejano

et al. 2018

J Software Evolu Process

View full text Add to dashboard Cite

Summary In this article, a strategy that seeks to assist the arduous cognitive process that involves understanding a GUI‐based system is presented. In order to reach this goal, a UML use case model with the most relevant features is obtained. To derive this model, the strategy performs the following steps: Extraction and filtering of specific system static information and implementation of a clustering process that inspects this information including GUI widgets, which are components closely related to the system problem domain. Although these steps are commonly known and used in the context of reverse engineering, the strategy introduces unusual approaches regarding the proposals found in the available literature. More specifically, the strategy presents (a) a set of metrics that infers the relative importance of a method or a function within the analysed system, (b) a summarization process driven by different features of software systems, and (c) a technique to cluster software artefacts and to map the cluster model onto a use case model. The article also propose a methodology that allows to compare the achieved results. The assessment of the approach suggests that the strategy can assist the software engineer to understand a software system providing a useful fine‐grained use case model.

show abstract

Information-theoretic software clustering

Cited by 209 publications

References 24 publications

API-Based and Information-Theoretic Metrics for Measuring the Quality of Software Modularization

API-Based and Information-Theoretic Metrics for Measuring the Quality of Software Modularization

Clustering large software systems at multiple layers

Using reverse engineering techniques to infer a system use case model

Contact Info

Product

Resources

About