The unified modeling language(UML) is one of the most commonly used modeling languages in the software industry.It simplifies the complex process of design by providing a set of graphical notations,which helps express...The unified modeling language(UML) is one of the most commonly used modeling languages in the software industry.It simplifies the complex process of design by providing a set of graphical notations,which helps express the objectoriented analysis and design of software projects.Although UML is applicable to different types of systems,domains,methods,and processes,it cannot express certain problem domain needs.Therefore,many extensions to UML have been proposed.In this paper,we propose a framework for integrating the UML extensions and then use the framework to propose an integrated unified modeling language-graphical(iUML-g) form.iUML-g integrates the existing UML extensions into one integrated form.This includes an integrated diagram for UML class,sequence,and use case diagrams.The proposed approach is evaluated using a case study.The proposed iUML-g is capable of modeling systems that use different domains.展开更多
Software metrics are used to measure different attributes of software. To practically measure software attributes using these metrics, metric thresholds are needed. Many researchers attempted to identify these thresho...Software metrics are used to measure different attributes of software. To practically measure software attributes using these metrics, metric thresholds are needed. Many researchers attempted to identify these thresholds based on personal experiences. However, the resulted experience-based thresholds cannot be generalized due to the variability in personal experiences and the subjectivity of opinions. The goal of this paper is to propose an automated clustering framework based on the expectation maximization (EM) algorithm where clusters are generated using a simplified 3-metric set (LOC, LCOM, and CBO). Given these clusters, different threshold levels for software metrics are systematically determined such that each threshold reflects a specific level of software quality. The proposed framework comprises two major steps: the clustering step where the software quality historical dataset is decomposed into a fixed set of clusters using the EM algorithm, and the threshold extraction step where thresholds, specific to each software metric in the resulting clusters, are estimated using statistical measures such as the mean (μ) and the standard deviation (σ) of each software metric in each cluster. The papers findings highlight the capability of EM-based clustering, using a minimum metric set, to group software quality datasets according to different quality levels.展开更多
基金supported by the King Fahd University of Petroleum and Minerals,Saudi Arabia(No.IN100046)
文摘The unified modeling language(UML) is one of the most commonly used modeling languages in the software industry.It simplifies the complex process of design by providing a set of graphical notations,which helps express the objectoriented analysis and design of software projects.Although UML is applicable to different types of systems,domains,methods,and processes,it cannot express certain problem domain needs.Therefore,many extensions to UML have been proposed.In this paper,we propose a framework for integrating the UML extensions and then use the framework to propose an integrated unified modeling language-graphical(iUML-g) form.iUML-g integrates the existing UML extensions into one integrated form.This includes an integrated diagram for UML class,sequence,and use case diagrams.The proposed approach is evaluated using a case study.The proposed iUML-g is capable of modeling systems that use different domains.
文摘Software metrics are used to measure different attributes of software. To practically measure software attributes using these metrics, metric thresholds are needed. Many researchers attempted to identify these thresholds based on personal experiences. However, the resulted experience-based thresholds cannot be generalized due to the variability in personal experiences and the subjectivity of opinions. The goal of this paper is to propose an automated clustering framework based on the expectation maximization (EM) algorithm where clusters are generated using a simplified 3-metric set (LOC, LCOM, and CBO). Given these clusters, different threshold levels for software metrics are systematically determined such that each threshold reflects a specific level of software quality. The proposed framework comprises two major steps: the clustering step where the software quality historical dataset is decomposed into a fixed set of clusters using the EM algorithm, and the threshold extraction step where thresholds, specific to each software metric in the resulting clusters, are estimated using statistical measures such as the mean (μ) and the standard deviation (σ) of each software metric in each cluster. The papers findings highlight the capability of EM-based clustering, using a minimum metric set, to group software quality datasets according to different quality levels.
