No abstract

No abstract

In our paper, we applied a non-pheromone based intelligent swarm optimization technique namely artificial bee colony optimization (ABC) for test suite optimization. Our approach is a population based algorithm, in which each test case represents a possible solution in the optimization problem and happiness value which is a heuristic introduced to each test case corresponds to the quality or fitness of the associated solution. The functionalities of three groups of bees are extended to three agents namely Search Agent, Selector Agent and Optimizer Agent to select efficient test cases among near infinite number of test cases. Because of the parallel behavior of these agents, the solution generation becomes faster and makes the approach an efficient one. Since, the test adequacy criterion we used is path coverage; the quality of the test cases is improved during each iteration to cover the paths in the software. Finally, we compared our approach with Ant Colony Optimization (ACO), a pheromone based optimization technique in test suite optimization and finalized that, ABC based approach has several advantages over ACO based optimization .

No abstract

Software development organizations spend considerable portion of their budget and time in testing related activities. The effectiveness of the verification and validation process depends upon the number of errors found and rectified before releasing the software to the customer side. This in turn depends upon the quality of test cases generated. The solution is to choose the most important and effective test cases and removing the redundant and unnecessary ones; which in turn leads to test case optimization. To achieve test case optimization, this paper proposed a heuristics guided population based search approach namely Hybrid Genetic Algorithm (HGA) which combines the features of Genetic Algorithm (GA) and Local Search (LS) techniques to reduce the number of test cases by improving the quality of test cases during the solution generation process. Also, to evaluate the performance of the proposed approach, a comparative study is conducted with Genetic Algorithm and Bacteriologic Algorithm (BA) and concluded that, the proposed HGA based approach produces better results.

This paper presents digital image processing and its representation using binary image; grayscale, color images with the help of additive color mixing, subtractive color mixing, and histogram. It is also discusses the fundamental steps involved in an image processing such as image achievement, image development, image renovation, compression, wavelets, multi-resolution processing, morphological processing, representation, description and interpretation. Finally, it presents the per-pixel and filtering operations like invert filter, grayscale, brightness and color splitting filter.

Medical imaging has a great impact in the field of diagnosis and surgical planning. The coding of medical images

Our paper focuses on the generation of optimal test sequences and test cases using Intelligent Agents for highly reliable systems. Test sequences support test case generation for these types of systems. Our system is modeled through UML state charts. Conventional test generation techniques do not worry about optimization and dynamic nature of such systems. In the case of highly reliable Software Testing, we can implement agents with sophisticated intellectual capabilities such as the ability to reason, learn, or plan. In our proposed approach, we developed agents namely Intelligent Search Agent (ISA) for optimal test sequence generation and Intelligent Test Case Optimization Agent (ITOA) based on HGA for optimal test case generation. Finally, we compared our results against existing algorithms. We registered our tool "IntelligenTester" under Java Research License (JRL) under the URL name https://intelligentester.dev.java.net.