An Improved & Adaptive Software Development Methodology

The methods of software development have increased a lot from the beginning. From the first waterfall to current agile methodology there still have some drawbacks. For this reason, the software delivery is still a very challenging and heavy-duty work. In this paper, we proposed a new software development methodology which is easy to implement and will help software development companies a secure and robust software releases. The proposed SDLC process is known as 4A. The empirical result shows that the proposed methodology is more adaptive and flexible for developers and project managers

Adaptive persistence layer for synchronous replication (PLSR) in heterogeneous system

Nowadays, in the grid community, distributed and clustering system, a lot of work has been focused on providing efficient and safe replication management services through designing of algorithms and systems. For many reasons, businesses or specially enterprise business or industrial business use replication. Therefore, replication is a useful technique for distributed systems. It can improve the performance and the reliability of a database application. In addition, it can be considered as a data backup method in case of hardware failure, software corruption or even a natural disaster. A change of the main database is reflected, forwarded and applied at each of the replicated server which might be in a remote location. Replication in the heterogeneous system is a very promising and challenging platform which is a compound of multi environment. Proper mechanism is significantly required in order to manage the complex heterogeneous data replication. In this research, Persistence Layer for Synchronous Replication (PLSR) has been proposed to support heterogeneous systems. The main objective of this technique is to develop an adaptive persistence layer which consisted of reliable and smooth replication. This technique also introduces a multi thread based persistence layer, which supports early binding and parallel connection to the servers. All the replication servers established its connection through interfaces. Furthermore, similar with the Service Oriented Architecture (SOA) and the structure is flexible enough to modify i.e.; adding and removing replication server. The PLSR is proposed based on the multithreading technique in order to avoid the dependency of replicated server from the main server and to make the enterprise software more enhanced so that the system will never be unstable during system up-gradation or system crashes. Consequently,the implementation of this technique will be applicable to enterprise application such as bank, insurance, group of companies as well as a small and medium organization such as NGO. The new replication process will also be used in e-commerce application to secure user transaction information. The motivation of implementation is to make sure the data replication is easy to maintain and cost effective. The PLSR architecture model, workflow and algorithms are described. The PLSR has been developed using Java Programming language. The system requirements also have been elaborated. The experimental main server and replication servers were established in Windows and Linux platform using the local area network (LAN). Finally, series of experiments have been carried out by using different servers. The snapshot of implementation showed that the proposed framework works successfully with replicating data in different operating systems. The result shows that PLSR performs outstandingly and the value is 83.2 % and 2.49% than SQL server for transactional insert and synchronization in compare to time (seconds)

Managing Heterogeneous Database Replication Using Persistence Layer Synchronous Replication (PLSR)

Distributed database replication in the heterogeneous system is a very promising and challenging platform which is the compound of multi environment. Proper mechanism is significantly required in order to manage the complex heterogeneous data replication. This paper presents a new algorithm namely the Persistence Layer Synchronous Replication (PLSR) in order to manage the agent handling the replication. The main objective of this algorithm is to develop an adaptive persistence layer which consisted of reliable and smooth replication. It achieves faster time execution and cost minimization than that other replication processes. This algorithm also introduces a multi thread based persistence layer, which supports early binding and parallel connection to the servers. All the replication servers establish its connection through interfaces, which is furthermore, similar with the SOA (Service Oriented Architecture) and the structure is flexible enough to modify i.e.; adding and removing replication server. The performance has been compared with SQL Server in terms of transactional inserts and synchronization time. The result shows that PLSR performs outstandingly up to 83.2 % and 2.49% than that SQL server for transactional insert and time synchronization, respectively

HeMI ++: a genetic algorithm based clustering technique for sensible clusters

Comunicació presentada al IEEE Congress on Evolutionary Computation (CEC 2020), celebrat del 19 al 24 de juliol de 2020 a Glasgow, Escòcia.We propose a new clustering technique called HeMI++. It uses cleansing and cloning operations that help to produce sensible clusters. HeMI++ learns necessary properties of a good clustering solution for a dataset from a high-quality initial population, without requiring any user input. It then disqualifies the chromosomes that do not satisfy the properties through its cleansing operation. In the cloning operation, HeMI++ replaces the chromosomes by high-quality chromosomes already found in the initial population. We compare HeMI++ with six (6) existing techniques on twenty (20) publicly available datasets using the Tree Index metric. Our experimental results indicate a clear superiority of HeMI++ over existing methods. We also apply HeMI++ on a brain dataset and demonstrate its ability to produce sensible clusters

MT2Way: A novel strategy for pair-wise test data generation

Reducing the number of test cases by utilizing minimum possible amount of time during the testing process of software and hardware is highly desirable. For ensuring the reliability of the method the combination of a complete set of available inputs is recommended to be executed. But generally an exhaustive numbers of test cases are hard to execute. Besides, test data generation is an NP-hard (non-deterministic polynomial-time hard) problem. This is likely to present considerable difficulties in defining the best possible method for generating the test data. The reduction of test cases depends on the interaction level, 2-way interaction or pair-wise test data can reduce high number of test cases and it efficiently addresses most of the software errors. This paper presents MT2Way, an effective 2-way interaction algorithm to generate the test data which is more acceptable in terms of the number of test cases and execution time. The performance tests show that MT2Way achieve better results in terms of system configuration, generated test size, and executing time as compared to other techniques

MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters

K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. The main drawback of this algorithm is that user should specify the number of cluster in advance. As an iterative clustering strategy, K-Means algorithm is very sensitive to the initial starting conditions. In this paper, we propose a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. MaxD K-means also used a novel strategy of setting the initial centroids. The experiment of the Max-D means has been conducted using synthetic data, which is taken from the Llyod’s K-Means experiments. The results from the new algorithm show that the number of iteration improves tremendously, and the number of iterations is reduced by confirming an improvement rate is up to 78%

Genetic algorithm with healthy population and multiple streams sharing information for clustering

Many popular clustering techniques including K-means require various user inputs such as the number of clusters k, which can often be very difficult for a user to guess in advance. Moreover, existing techniques like K-means also have a tendency of getting stuck at local optima. As a result, various evolutionary algorithm based clustering techniques have been proposed. Typically, they choose the initial population randomly, whereas carefully selected initial population can improve final clustering results. Hence, some existing techniques such as GenClust carefully select high-quality initial population with a complexity of O(n2) which is very high. We propose a clustering technique that in addition to selecting an initial population with a low complexity of O(n), uses a number of new components including multiple streams, information exchange between neighboring streams, regular health improvement of the chromosomes, and mutation which also aims to improve chromosome health. We compare the proposed technique HeMI with five (5) existing techniques on 20 publicly available data sets in terms of two well-known evaluation criteria. We also carry out a thorough experimentation to investigate the usefulness of the new components of HeMI. Our experimental results demonstrate statistically significant superiority of HeMI over existing techniques and the effectiveness of the proposed components

Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters

K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points

Framework of Persistence Layer Synchronous Replication to Improve Data Availability into a Heterogeneous System

Data replication is an important technique in peer to peer network, data grid architecture, clustering and distributed system, where it increases data availability and enhances data access and reliability and minimizes the cost of data transmission. In this paper, we proposed a framework and structure of synchronous replication from the persistence layer that supports heterogeneous system. In this framework, we developed the multithreading based persistence layer. Our objective is to make the persistence layer more adaptive. In this adaptive persistency system, the replication server will not depend on the main server, so forth, adding a new replication server will be easier than ever, easy to cope with heterogeneous system, cost minimizing and finally there will be no down time