Object structure

Title:

Genetic granular classifiers in modeling software quality

Subtitle:

Raport Badawczy = Research Report ; RB/5/2003

Creator:

(1953- ). Autor ; Succi, Giancarlo. Autor

Publisher:

Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences

Place of publishing:

Warszawa

Date issued/created:

2003

Description:

15 pages ; 21 cm ; Bibliography p. 14-15

Subject and Keywords:

Genetic algorithms ; Algorytm genetyczny ; Grupowanie rozmyte ; Software quality ; Hyperbox geometry of classifiers ; Sotfware measures ; Fuzzy c-means ; Jakość oprogramowania

Abstract:

Hyberbox clasifiers are one of the most appealing and intuitively transparent classification schemes. As the name stipulates, these classifiers are based on a collection of hyperboxes – generic and highly interpretable geometric descriptors of data belonging to a given class. The hyperboxes translate into conditional statements (rules) of the form “if feature1 is in [a,b] and feature2 is in [d,f] and … and featuren is in [w,z] then class ω’’ where the intervals ([a,b], …, [w,z]) are the respective edges of the hyperbox. The proposed design process of hyperboxes comprises of two main phases. In the first phase, a collection of “seeds” of he hyperboxes is formed through data clustering (realized by means of the Fuzzy C-Means algorithm, FCM). In the second phase, the hyperboxes are ‘grown” by applying mechanisms of genetic optimization (and genetic algorithm, in particular). We reveal how the underlying geometry of the hyperboxes supports an immediate interpretation of software data concerning software maintenance and dealing with rules describing a number of changes made to software modules and their linkages with various software measures (such as size of code, McCabe cyclomatic complexity, number of comments, number of characters, etc.)Hyberbox clasifiers are one of the most appealing and intuitively transparent classification schemes. As the name stipulates, these classifiers are based on a collection of hyperboxes – generic and highly interpretable geometric descriptors of data belonging to a given class. The hyperboxes translate into conditional statements (rules) of the form “if feature1 is in [a,b] and feature2 is in [d,f] and … and featuren is in [w,z] then class ω’’ where the intervals ([a,b], …, [w,z]) are the respective edges of the hyperbox. The proposed design process of hyperboxes comprises of two main phases. In the first phase, a collection of “seeds” of he hyperboxes is formed through data clustering (realized by means of the Fuzzy C-Means algorithm, FCM). In the second phase, the hyperboxes are ‘grown” by applying mechanisms of genetic optimization (and genetic algorithm, in particular). We reveal how the underlying geometry of the hyperboxes supports an immediate interpretation of software data concerning software maintenance and dealing with rules describing a number of changes made to software modules and their linkages with various software measures (such as size of code, McCabe cyclomatic complexity, number of comments, number of characters, etc.)Hyberbox clasifiers are one of the most appealing and intuitively transparent classification schemes. As the name stipulates, these classifiers are based on a collection of hyperboxes – generic and highly interpretable geometric descriptors of data belonging to a given class. The hyperboxes translate into conditional statements (rules) of the form “if feature1 is in [a,b] and feature2 is in [d,f] and … and featuren is in [w,z] then class ω’’ where the intervals ([a,b], …, [w,z]) are the respective edges of the hyperbox. The proposed design process of hyperboxes comprises of two main phases. In the first phase, a collection of “seeds” of he hyperboxes is formed through data clustering (realized by means of the Fuzzy C-Means algorithm, FCM). In the second phase, the hyperboxes are ‘grown” by applying mechanisms of genetic optimization (and genetic algorithm, in particular). We reveal how the underlying geometry of the hyperboxes supports an immediate interpretation of software data concerning software maintenance and dealing with rules describing a number of changes made to software modules and their linkages with various software measures (such as size of code, McCabe cyclomatic complexity, number of comments, number of characters, etc.)Hyberbox clasifiers are one of the most appealing and intuitively transparent classification schemes. As the name stipulates, these classifiers are based on a collection of hyperboxes – generic and highly interpretable geometric des

Relation:

Raport Badawczy = Research Report

Resource Type:

Report

Source:

RB-2003-05

Language:

eng

Language of abstract:

eng

Rights:

Creative Commons Attribution BY 4.0 license

Terms of use:

Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

Digitizing institution:

Systems Research Institute of the Polish Academy of Sciences

Original in:

Library of Systems Research Institute PAS

Projects co-financed by:

Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.


External content

This content is hosted outside the digital library.

Click the link below to view the content.

https://www.ibspan.waw.pl/~alex/OZwRCIN/WA777_0_RB-2003-05_Genetic%20granular%20classifiers%20in%20modeling%20software%20quality_content.pdf
×

Citation

Citation style: