z-logo
open-access-imgOpen Access
Empirical and Theoretical Validation of a Use Case Diagram Complexity Metric
Author(s) -
Sangeeta Sabharwal,
Preeti Kaur,
Ritu Sibal
Publication year - 2017
Publication title -
international journal of information technology and computer science
Language(s) - English
Resource type - Journals
eISSN - 2074-9015
pISSN - 2074-9007
DOI - 10.5815/ijitcs.2017.11.04
Subject(s) - computer science , artifact (error) , metric (unit) , dependency (uml) , diagram , use case diagram , key (lock) , software metric , quality (philosophy) , class diagram , data mining , theoretical computer science , software development , software , software quality , artificial intelligence , programming language , unified modeling language , operations management , database , economics , philosophy , computer security , epistemology
A key artifact produced during object oriented requirements analysis is Use Case Diagram. Functional requirements of the system under development and relationship of the system and the external world are displayed with the help of Use Case Diagram. Therefore, the quality aspect of the artifact Use Case Diagram must be assured in order to build good quality software. Use Case Diagram quality is assessed by metrics that have been proposed in the past by researchers, based on Use Case Diagram countable features such as the number of actors, number of scenarios per Use Case etc., but they have not considered Use Case dependency relations for metric calculation. In our previous paper, we had proposed a complexity metric. This metric was defined considering association relationships and dependency prevailing in the Use Case Diagram. The key objective in this paper is to validate this complexity metric theoretically by using Briand‟s Framework and empirically by performing a Controlled experiment. The results show that we are able to perform the theoretical and empirical validation successfully.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom