Predicting Quality of Object-Oriented Systems through a Quality Model Based on Design Metrics and Data Mining Techniques

2009 ◽  
Author(s):  
Chuan Ho Loh ◽  
Sai Peck Lee
Keyword(s):  
Data Mining ◽  
Object Oriented ◽  
Quality Model ◽  
Design Metrics ◽  
Data Mining Techniques ◽  
Model Based ◽  
Object Oriented Systems

scholarly journals Software Reusability of Object-Oriented Systems using Data Mining Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 2144-2152
Keyword(s):  
Data Mining ◽  
Software Reuse ◽  
Object Oriented ◽  
Market Value ◽  
Software Systems ◽  
Data Mining Techniques ◽  
Software Reusability ◽  
Using Data ◽  
Object Oriented Systems

Due to fast advancement in software industry, there was a demand to cut down time and efforts during process of software development. While designing product and services it is very essential to assure quality of product in order to strengthen market value of the product. To accomplish both quality as well as productivity objectives, it is suggested to go for software reuse. Reusability is an essential measure that can be used to improve overall software quality with lesser cost and efforts. This paper gives insights into various literature studies related to software reusability of Object-oriented software using data mining techniques. In this paper even comparative analysis of various techniques related to prediction and enhancement of reusability of Object-Oriented software systems has been done. This would help to get better understanding of need of reusability enhancement of Object-Oriented systems using data mining techniques

DESIGN OPTIMIZATION METRICS FOR UML BASED OBJECT-ORIENTED SYSTEMS

2007 ◽  
Vol 17 (03) ◽  
pp. 423-448 ◽  
Author(s):  
E. RAMARAJ ◽  
S. DURAISAMY
Keyword(s):  
Software Metrics ◽  
Quality Evaluation ◽  
Unified Modeling Language ◽  
Object Oriented ◽  
Evaluation Process ◽  
Quality Model ◽  
Design Quality ◽  
Object Oriented Systems ◽  
Oriented System

Design plays a key role in the development of software. The quality of design is crucial and is a fundamental decision element in assessing the software product. The early availability of design quality evaluation provides a better way to decide the quality of the final product. This avoids presumption in the quality evaluation process. Hence Software Metrics provide a valuable and objective insight of enhancing each of the software quality characteristics. This paper proposes a quality model to assess the design phase of any object-oriented system based on the works of Chidamber, Kemrer and Basili and suggests two new metrics. The research focuses on analyzing a set of metrics, which has direct influence on the quality of the software and creating a metrics tool based on Java that can be used to validate the object-oriented projects against these metrics. The analysis is carried out on a set of real world projects designed using Unified Modeling Language, which are used as test cases. These metrics and models are proposed to add more quality information in refining any object-oriented system during the early stages of design itself.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Effective Intelligent Data Mining Using Dempster-Shafer Theory

2008 ◽  
pp. 2943-2963
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Missing Values ◽  
Uncertain Reasoning ◽  
Worst Case ◽  
Data Mining Techniques ◽  
Imperfect Data ◽  
Dempster Shafer Theory ◽  
External Management ◽  
Shafer Theory

The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is the quality of the data analysed, including whether the data is imprecise or in the worst case incomplete. Through the description of Dempster-Shafer theory (DST), a general methodology based on uncertain reasoning, it argues that traditional data mining techniques are not structured to handle such imperfect data, instead requiring the external management of missing values, and so forth. One DST based technique is classification and ranking belief simplex (CaRBS), which allows intelligent data mining through the acceptance of missing values in the data analysed, considering them a factor of ignorance, and not requiring their external management. Results presented here, using CaRBS and a number of simplex plots, show the effect of managing and not managing of imperfect data.

scholarly journals Improving Quality of Educational Processes Providing New Knowledge Using Data Mining Techniques

2014 ◽  
Vol 147 ◽  
pp. 390-397 ◽  
Author(s):  
Manolis Chalaris ◽  
Stefanos Gritzalis ◽  
Manolis Maragoudakis ◽  
Cleo Sgouropoulou ◽  
Anastasios Tsolakidis
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Educational Processes ◽  
New Knowledge ◽  
Using Data

Creation of Fuzzy Control Table with Data Mining

2013 ◽  
Vol 760-762 ◽  
pp. 1080-1083
Author(s):  
Jun Gao
Keyword(s):  
Data Mining ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Association Rule ◽  
New Method ◽  
Data Mining Techniques ◽  
Fuzzy Control System

A good fuzzy control table is the key to a fuzzy control system, and the systems performance mainly depends on the quality of the table. Based on analyzing fully the principles of a typical fuzzy control systems and the procedures of building a fuzzy control table, this paper presents a new method of applying the boolean association rule data mining techniques to mining of fuzzy control table directly from the database of manual operating records.

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