Fault Prediction Approach

Project Report from the year 2019 in the subject Computer Science - Software, grade: A, , course: Doctoral Degree, language: English, abstract: This research works seeks to explore and provide an improved fault detection approach for inspection and fault detection. It systematically investigate and characterize software faults and faults to improve fault detection and prevention mechanisms in the quality software development process. Firstly, it contributes an Adaptive PSO-based association rule mining techniques for software fault classification using ANN. This task categorizes real defects by finding the best support and reliability to have the best policy for software fault classification using ANN. Secondly, it provides a Fault Prediction Approach (FPA) based on probabilistic models to perform software testing in Software Inspection. This describes a cost-effective way to accurately detect the defects by performing software inspection to develop quality software. The proposed FPA probes stochastic methods using the modified Naive Bayes classification to estimate the possible faults in the experimental environment to suggest novel defect control development.

Software reliability engineering has become very important as the complexity of the system has increased exponentially with technological advances. The fact that all systems today depend on many other systems and interfaces is not only an application error but also a number of environmental factors that lead to failure. The impact of these failures depends on the nature of the system, but many of them cause customer dissatisfaction and business loss. System testing and fault detection have become the most important processes in the software life cycle. Various failure prediction models can be analyzed and suggested so that failures can be detected at an early stage and many test efforts can be saved. Software development has many defects in the design phase. In the past, many examples of software development deficits have been presented. This practice demonstrates various effects on defect reduction.

Methodology for defect detection and better maintenance strategies that can be used to support defective or non-defective software modules before they are deployed to all software projects.