Abstract Database plays very crucial role in software development. A well-organized database provides a back-end with correct access of information and least possibility of failure. Software development can be accomplished with Quality Function Deployment for better results. Application of Quality Function Deployment will assist in identification and rectification of problems and improvement of software product quality. Employing traditional Quality Function Deployment has a major... Database plays very crucial role in software development. A well-organized database provides a back-end with correct access of information and least possibility of failure. Software development can be accomplished with Quality Function Deployment for better results. Application of Quality Function Deployment will assist in identification and rectification of problems and improvement of software product quality. Employing traditional Quality Function Deployment has a major drawback as it works on a crisp approach. A wrong conclusion can easily be drawn out since the nature of linguistic terms is disregarded. Integration of Fuzzy with Quality Function Deployment can be of great use to overcome this issue. As Quality Function Deployment matrix calculations are very complicated to handle manually, thus database is sturdily required. This will help database designer in understanding the formation of software tool to improve the quality of software product using Fuzzy Quality Function Deployment. This paper presents the literature of the pertaining topic followed by the brief discussion on Quality Function Deployment and Fuzzy Quality Function Deployment. Software database formation and workings are presented. Here the value chain needed is presented. Followed by the flow chart which shows how the process will be carried out. Then the detailed database tables are discussed. At last we have presented the entity relationship diagram which relates each entity with other. Read More Read Less

Abstract Refactoring is the process of improving code quality without affecting its external behaviour and by changing its internal structure. Refactoring is done to improve code quality and structure. In this research, we have proposed a new method of code refactoring by using graph theory techniques. Previously manual methods were used to identify the classes with high impact in refactoring. The proposed method has... Refactoring is the process of improving code quality without affecting its external behaviour and by changing its internal structure. Refactoring is done to improve code quality and structure. In this research, we have proposed a new method of code refactoring by using graph theory techniques. Previously manual methods were used to identify the classes with high impact in refactoring. The proposed method has been implemented and applied on selected software projects developed in java. Validation has been done by surveying software professionals to measure improve in code quality. Read More Read Less

Abstract Data Analysis is getting more and more important in Today’s world. If data of a person gets lost then, it will be a loss of his/her identity so maintain a big or huge amount of data without loosing, is a challenge. Data clustering can do that because it can maintain a huge amount of data by dividing it into various... Data Analysis is getting more and more important in Today’s world. If data of a person gets lost then, it will be a loss of his/her identity so maintain a big or huge amount of data without loosing, is a challenge. Data clustering can do that because it can maintain a huge amount of data by dividing it into various clusters. Clustering is separation of similar data from dissimilar content. This paper reviews the data clustering techniques used to separate large amount of data. Motivation for this paper comes from an enormous amount of data in clusters and finding a small errors in them. The review shows that Incremental clustering is becoming a significant problem because the data is generating in an enormous amount. The data can be of various types like it could be in the form of text, spatial data, images, sequence data, data in the form of streams, multimedia data. To manage such kinds of data, data mining has various techniques. Affinity Propagation (AP) is one of the methods that has use in much incremental clustering problems. Most recent approaches reduce the data content by various methods such as compressed model that combines horizontal compression with vertical compression. The review covers incremental clustering, Affinity propagation to maintain a large amount of data and other methods regarding clustering. The analysis shows a trend towards reducing a data into small size so the data analyzation will become accessible. Data is information and to take out the knowledge from that information is important, so that mining of data will become efficient. Read More Read Less