Kavita Choudhary

Glaucoma Detection Using Cross Validation Algorithm

This paper is basically extension of my work on glaucoma detection. This can be achieved by various data mining techniques and algorithms such as Association, Classification, Clustering, Fuzzy decision tree etc. The objective of this paper is early detection of glaucoma using cross validation algorithm. We will analyses the symptoms shown by different patients and compute and generalize them to reach a decision. This will help not only patients but doctors also to make it primarily practice to follow for new patients.

Prediction of Occurrence of Heart Disease and Its Dependability on RCT Using Data Mining Techniques

This paper is basically extension of our work in detection of diseases using data mining techniques. Here it shows that heart disease can be diagnose by using various data mining techniques and algorithms such as decision tree, split validation and apply model. Some Attributes like age, root canal treatment, smoking and diabetes predicts the probability of patients getting a heart disease. The proposed system that is the union of computer-based patient records with clinical decision support can lower down medical mistakes, unwanted practice variation and helps in improving patient safety and outcome.

MOGA-UDTG: Automated uniformly distributed testing approach

Software Testing is the critical task for any software which helps in building trust and confidence in the product. Usually, the emphasis is on Manual Testing but to reduce cost there is a need of automation in software testing process. This paper proposes a method to generate automated uniformly distributed test cases based on the platform of multi-objective genetic algorithm (MOGA). The proposed method is analyzed on the domain of Pareto-Optimality, Dominance and Multi-objective Genetic Algorithm.

Cuckoo Search in Test Case Generation and Conforming Optimality Using Firefly Algorithm

To accomplish the effectual software testing there is a requirement for optimization of test cases. The most challenging task in software testing is the generation of optimal test cases. There are various methods that are being used for generation of test cases and the test case optimization. The paper manifests the two different algorithms for test case generation and optimization of those test cases. The algorithms discussed are based on multi-objective optimization technique and successfully shows the desired results. The Cuckoo search algorithm based on the breeding behavior of Cuckoo bird is used here for the generation of test cases for a discussed problem and another algorithm based on the flashing phenomenon of fireflies is used for the optimization of the generated test cases. The second algorithm used verifies if every node in the given control flow graph is covered by given test cases.

Path Reliability in Automated Test Case Generation Process

In software testing, the reliability of a path is an important factor and must be calculated to go forth in the testing process. This paper is the extended work (Cuckoo Search in Test Case Generation and Conforming Optimality using Firefly Algorithm, 2015) [1], previously test cases are generated using cuckoo search. The reliability is calculated with the help of control flow graphs and mathematical calculations for all the paths specified in flow graph (Integrating Path Testing with Software Reliability Estimation Using Control Flow Graph, 2008) [2]. We consider various test cases that are traversing different paths and accordingly compute reliability of the paths.

Multi-objective Optimization Based Software Testing Using Kansei Quality Approach

Software testing suggests the quality of the software product. More the effective testing means high quality software product. In this paper we have identified and prioritized the parameters according to the perspective of different entities involved in software lifecycle. The prime objective of the paper is to perform software testing from the perspective of Kansei Engineering methodology with multi-objective optimization.

Automated Uniformly Distributed Test Case Generation (MO-UDTG) Using Multiobjective Optimization

Software Testing is the most challenging area of Software Engineering which usually gains consumers trust and confidence in the product. Software testing is time consuming, tedious and very expensive task. There is a need of automation in software testing process. This paper proposes a method to generate automated uniformly distributed test cases based on the platform of multi-objective (MO).