Rajender Singh Chhillar

A New Load Balancing Technique for Virtual Machine Cloud Computing Environment

Cloud computing is one of the latest and upcoming paradigm that offers huge benefits such as reduced time to market, unlimited computing power and flexible computing capabilities. It is a model that provides an on-demand network access to a shared pool of computing resources It comprises a large number of concepts primarily Load Balancing, Scheduling, etc. This paper discusses load balancing as a mechanism to distribute the workload evenly to all nodes in the system to achieve a higher resource utilization and user satisfaction. It helps in allocation and de-allocation of instances of applications without failure. This paper reports a new load balancing technique and its comparison with round robin providing encouraging results.

A Novel Technique for Generation of Test Cases based on Bee Colony Optimization and Modified Genetic Algorithm (BCOmGA)

Software testing is the most important phase of software development life cycle which ensures the quality of software systems. This paper proposes a novel approach for generation of test cases from “Unified Modeling Language” (UML). The Test Case Selection and Reduction is done by using Stratified Sampling, Bee Colony Optimization and Genetic Algorithm. This aims to save cost, time and effort by efficiently minimizing the test suite to ensure maximum coverage. We have applied this technique to a module of a Card Administration System project taken from Software Company. The result shows that the proposed approach effectively detects all the flaws by covering all possible paths of the system. The proposed software testing technique (BCO-mGA) ensures maximum coverage in minimum possible timeframe by executing the final minimized test suite.

A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques

Data warehousing and Data mining find enormous applications; RFID technology is one among them. A RFID data warehousing system with novel data cleaning, transformation and loading technique has been proposed in the previous work. The system has been dedicatedly implemented in one of the significant RFID applications tracking of goods in warehouses. The warehoused RFID data is in specific format and so an effective mining system is required to mine the needed information from the database. The existing mining algorithms are inefficient in extracting the information from the warehoused RFID data. In this paper, a novel data mining system is proposed, which effectively extracts the information regarding the nature of movement of the RFID tags. The proposed mining system generates an intermediate dataset (I-dataset) from the warehoused dataset. From the I-dataset, sequential patterns are mined with different pattern length combinations. From the mined sequential patterns, fuzzy rules are generated, which depicts the nature of movement of the RFID tags. The implementation results show that the proposed mining system performs well by extracting the significant RFID tags and its combinations and the nature of movement of the tags.

A Review: Study of Test Case Generation Techniques

Software testing is very labor intensive task for developing software and improving its quality. According to some researchers and software professionals,50% of the time, cost and effort are spent on software testing.To test software, generating test cases is the most important task.Testing can be done either manually or automatically by using various testing tools. In today’s scenario software are testedautomatically with the help of tools as it is a fast and accurate process of testing software. Although various testing tools are available in market and are used by testers to test the software and to generate test cases and test data automatically.There are various techniques available for generating test cases like fuzzy logic, finite state machine, neural networks, genetic algorithms, soft computing, genetic programming, evolutionary computation and many others.This paper presents various test case generation methods, test case minimization, selection, and prioritization and evaluation techniques.This paper also focuses on various test case prioritization and selection techniques that help the test engineers to schedule and rank the test cases to reduce the total effort, time and the cost.

Empirical analysis of object-oriented design metrics for predicting high, medium and low severity faults using mallows C p

An object-oriented approach has become a commonly-used method in software-related activities. Many design metrics for object-oriented systems have been proposed and also employed for predicting and managing the quality of processes and products. To enhance the practical utility of object-oriented metrics in software industry, various researchers have tried to find relations between these metrics and fault proneness, but very few focus on relating them with the number-offaults in different levels as per their severity rating. In this study, empirical validation is carried out on object-oriented design metrics (i.e. Chidamber and Kemerer CK-metrics suite and source lines of codes) for predicting number-of-faults in different severity levels. Different statistical methods are used to analyze the data, including correlation.

Empirical study of root cause analysis of software failure

Root Cause Analysis (RCA) is the process of identifying project issues, correcting them and taking preventive actions to avoid occurrences of such issues in the future. Issues could be variance in schedule, effort, cost, productivity, expected results of software, performance parameters and customer satisfaction. RCA also involves collecting valid data, analyzing it, deriving metrics and finding root causes using RCA methods. In this paper we will do Root cause analysis of some severe software failures that happened in the past and of some failures in ongoing projects in the software Industry. We will also describe various RCA methods and processes used in the software Industry to reduce the chances of software failure.

Software Test Process, Testing Types and Techniques

Software testing is the most critical phase of the Software Development Life Cycle. Software under test goes through various phases, which as per the study are test analysis; test planning, test case/data/environment preparation, test execution, bug logging and tracking and closure. There is lot of research which has been done in past to optimize overall testing process with intent of improving quality of software in a minimum amount of time. After evaluating all available testing processes it has been found that different development models are used for different types of applications and different testing techniques are performed to test the same. Based on the research during the study of this paper, it has been analyzed that each company modifies their testing process as per the needs and performs testing based on the criticality of the applications. The most critical components of each application have to be tested thoroughly to ensure their functional, performance and security features are behaving as expected. This paper talks about ensuring the quality of all types of software applications by performing certain types of testing techniques and optimized software testing processes. As per the study and research done testing types can be categorized under three major testing techniques which are Functional, Performance and Security Testing and major software testing process called as Analysis, Preparation and Execution and closure.

Measuring bug complexity in object oriented software system

Bugs are inevitable in any software development life cycle. Most bugs are detected and removed in the testing phase. In software, we can classify bugs into two categories: (1) bugs of different severity, from a users perspective,(how much damage the bug does) and (2) bugs of different complexity(how much is the debugging time lag between detection and correction). Prior knowledge of bug distribution of different complexity can help project managers in allocating testing resources and tools. Various researchers have proposed models for determining the proportion of bugs present in software of different complexity but none of these models have been applied to object oriented software. In this paper, we have proposed a model that will determine the proportion of different bug complexity. The paper also suggests the suitability of the proposed model for a particular data set. We have taken two data sets based on object oriented methodology namely SQL for Python and SQuirreL SQL Client software developed under open source environment.

Lightweight Image Encryption Scheme for Multimedia Security

Due to abundant growth of multimedia application it becomes necessary to secure multimedia data. The scope of this paper is confined to secure multimedia data especially images. The major issue that exists in images is the presence of redundant data. The main focus of this paper is to design a new shuffling scheme that can eliminate redundant data .The new shuffling scheme has been implemented on different images. In this paper comparative analysis is done before and after applying new shuffling scheme with the existing PESH algorithm. The shuffling schemes are applied on pure white images in order to test its efficiency, as white images have maximum redundant data. The new scheme is designed for the light weight devices which require less computation power.

Mining Optimal Utility Incorporated Sequential Pattern from RFID Data Warehouse Using Genetic Algorithm

Today, identification of sequential patterns from a huge database sequence is a major problem in the field of KDD. In addition, if the entire set of sequential patterns existing in a large database is presented, the user may find it difficult to understand and employ the mined result. In order to overcome these issues, we propose an efficient data mining system to generate the most favorable sequential patterns. The proposed technique first generates datasets from the warehoused RFID data. Each mined pattern has distinct utility and the most favorable sequential patterns are generated from the mined sequential patterns by using Genetic Algorithm (GA). A fitness function is used in GA to find out the sequential pattern that provides maximum profit. The implementation result shows that the proposed mining system accurately extracts the important RFID tags and its combinations, nature of movement of the tags and the optimum sequential patterns.