Using Metrics to Predict OO Information Systems Maintainability

Abstract

The quality of object oriented information systems (OOIS) depends greatly on the decisions taken at early phases of their development. As an early available artifact the quality of the class diagram is crucial to the success of system development. Class diagrams lay the foundation for all later design work. So, their quality heavily affects the product that will be ultimately implemented. Even though the appearance of the Unified Modeling Language (UML) as a standard of modelling OOIS has contributed greatly towards building quality OOIS, it is not enough. Early availability of metrics is a key factor in the successful management of OOIS development. The aim of this paper is to present a set of metrics for measuring the structural complexity of UML class diagrams and to use them for predicting their maintainability that will heavily be correlated with OOIS maintainability.

On the Application of Information Entropy-based Multi-attribute Decision in UML Class Diagram Metrics

“…In order to highlight the comparative, the numerical results of this paper expanded ten times in Figure 1. Values of Understandability, analyzability and maintainability in Table 5, came from experiments of M. Genero [11][12][13][14][15][16][17][18][19][20]. …”

“…On the condition of getting permission from M. Genero, we also use the same former twenty-seven UML class diagrams related to bank information systems as material [11][12][13][14][15][16][17][18][19][20]. M. Genero, et al carry out some comprehensive controlled experiments [11][12][13][14][15][16][17][18][19][20].…”

“…During the last several years, many different metrics for class diagrams have been suggested [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. These metrics help software developers to analyze reliability, maintainability and complexity of systems in the early phase of the OO software lifecycle.…”

“…Genero also discusses a group of indicators to measure the complexity of class diagrams [11][12][13][14][15][16][17][18][19][20]. They are the respective total number of classes (NC), attributes (NA), methods (NM), associations relationships (NAssoc), aggregation relationships (NAgg), dependency relationships (NDep), generalization relationships (NGen), generalization hierarchies (NGenH), and aggregation hierarchies (NAggH); respective percentage of the NAssoc, NAgg, NDep, and NGen with respect to the NC whose results can be abbreviated as NAssocVC, NAggVC, NDepVC, and NgenVC respectively; the respective maximum DIT and Hagg value whose results can be abbreviated as MaxDIT and MaxHagg respectively.…”

Maintainability Predictors for Relational Database-Driven Software Applications: Extended Results From a Survey

“…These studies were further analyzed to assess if the datasets used in these studies completely or partially comprised relational database-driven software applications. Only three of these studies [3,5,8] had used relational database-driven software applications in their datasets for the purpose of their research. However, only one of these studies [5] presented a maintainability prediction model but did not provide its prediction accuracy; the other two studies only presented measures [3] and factors [8] that impacted upon software maintainability, without any associated maintainability prediction model.…”