Doktorarbeit / Dissertation, 2021
160 Seiten
This thesis aims to develop assessment models for software reliability at the late design phase. It aims to address the challenges of traditional reliability assessment approaches, which are often ineffective at this critical stage of development. The research delves into exploring the reliability of software systems during the design phase, aiming to create models that can more accurately predict and assess software reliability early in the development process.
Chapter 1: Introduction introduces the research problem, objectives, scope, and thesis organization. It outlines the limitations of current reliability assessment approaches, particularly during the design phase, and highlights the need for novel solutions.
Chapter 2: Literature Survey provides a comprehensive overview of existing research on software reliability assessment. It examines early assessment techniques, explores methods for assessing reliability during the design phase, and discusses existing research on reliability prediction models. This chapter establishes the context for the proposed research.
Chapter 3: Proposed Software Reliability Assessment Models presents the core contribution of the thesis. It details the proposed models, their evaluation methodologies, implementation strategies, and summarizing the key features and benefits of these models.
Chapter 4: Case Study demonstrates the practical application of the proposed models. This chapter describes the case study scenario, outlines the implementation process, analyzes the results, and draws conclusions based on the empirical evidence.
Software reliability, reliability assessment, design phase, reliability prediction models, software quality, software engineering, case study, empirical analysis.
The goal is to develop more accurate models for predicting software reliability during the late design phase, addressing the limitations of traditional assessment methods.
The thesis proposes hybrid models based on Neural Networks (NN), including a combination of NN with the Intelligent Water Drop (IWD) technique and Spiking Neural Networks (SNN) with IWD.
IWD is a swarm-based optimization algorithm used in this research for feature selection and defect detection to improve the accuracy of reliability assessments.
Assessing reliability at the design phase allows developers to identify potential defects early, reducing the cost and effort of fixing errors compared to later stages like testing or deployment.
The models were tested using various datasets from different software projects, containing metric values and actual software failure data to validate their predictive power.
Der GRIN Verlag hat sich seit 1998 auf die Veröffentlichung akademischer eBooks und Bücher spezialisiert. Der GRIN Verlag steht damit als erstes Unternehmen für User Generated Quality Content. Die Verlagsseiten GRIN.com, Hausarbeiten.de und Diplomarbeiten24 bieten für Hochschullehrer, Absolventen und Studenten die ideale Plattform, wissenschaftliche Texte wie Hausarbeiten, Referate, Bachelorarbeiten, Masterarbeiten, Diplomarbeiten, Dissertationen und wissenschaftliche Aufsätze einem breiten Publikum zu präsentieren.
Kostenfreie Veröffentlichung: Hausarbeit, Bachelorarbeit, Diplomarbeit, Dissertation, Masterarbeit, Interpretation oder Referat jetzt veröffentlichen!

