Home / Computer Science / Design and implementation of an intelligent assistant system for software assessment

Design and implementation of an intelligent assistant system for software assessment

 

Table Of Contents


Chapter ONE

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Research
1.9 Definition of Terms

Chapter TWO

2.1 Overview of Intelligent Assistant Systems
2.2 Evolution of Intelligent Assistant Systems
2.3 Types of Intelligent Assistant Systems
2.4 Applications of Intelligent Assistant Systems
2.5 Benefits of Intelligent Assistant Systems
2.6 Challenges in Developing Intelligent Assistant Systems
2.7 Artificial Intelligence in Intelligent Assistant Systems
2.8 Machine Learning in Intelligent Assistant Systems
2.9 Natural Language Processing in Intelligent Assistant Systems
2.10 Future Trends in Intelligent Assistant Systems

Chapter THREE

3.1 Research Methodology Overview
3.2 Research Design
3.3 Data Collection Methods
3.4 Sampling Techniques
3.5 Data Analysis Procedures
3.6 Ethical Considerations
3.7 Validity and Reliability
3.8 Limitations of the Methodology

Chapter FOUR

4.1 Data Analysis and Interpretation
4.2 Findings of the Study
4.3 Comparison with Existing Systems
4.4 Discussion on System Performance
4.5 User Feedback and Satisfaction
4.6 Recommendations for Improvement
4.7 Implications for Future Research
4.8 Contributions to the Field

Chapter FIVE

5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to Knowledge
5.4 Practical Implications
5.5 Recommendations for Practice
5.6 Recommendations for Further Research

Project Abstract

Abstract
The design and implementation of an intelligent assistant system for software assessment is a crucial step towards enhancing the efficiency and accuracy of evaluating software projects. This research project focuses on developing a system that utilizes artificial intelligence techniques to assist software developers, project managers, and quality assurance teams in assessing the quality, performance, and security aspects of software applications. The intelligent assistant system aims to automate the assessment process, provide valuable insights, identify potential issues, and offer recommendations for improvement. The system incorporates various AI technologies such as machine learning algorithms, natural language processing, and data mining techniques to analyze software artifacts, code repositories, and user feedback. By leveraging these technologies, the system can understand the context of the software being assessed, detect patterns, and predict potential risks or vulnerabilities. Additionally, the system can learn from past assessment results and continuously improve its accuracy and performance over time. Key features of the intelligent assistant system include automated code quality analysis, security vulnerability detection, performance profiling, and compliance checking. The system can generate detailed reports highlighting areas of concern, best practices violations, and suggestions for optimizing software performance. Moreover, the system can assist in tracking project milestones, monitoring progress, and identifying potential bottlenecks that may impact the overall software development lifecycle. The user interface of the intelligent assistant system is designed to be user-friendly, intuitive, and customizable to meet the specific needs of different software development teams. Users can interact with the system through a web-based interface, command-line interface, or integrated development environment plugin. The system provides real-time feedback, alerts, and notifications to keep users informed about the status of their projects and any critical issues that require immediate attention. Overall, the intelligent assistant system aims to streamline the software assessment process, improve decision-making, and enhance the overall quality of software projects. By combining the power of artificial intelligence with software assessment practices, the system offers a valuable tool for software development teams to increase productivity, reduce risks, and deliver high-quality software products to end-users.

Project Overview

INTRODUCTION

1.0 Introduction

While software projects have become large industrial production processes, it has been noticed that process assessment can be a strong and effective driver for process improvement. Based on this, acquirers of large, critical software-intensive systems have impeded for the use of international standard for process assessment. High-quality software is tightly connected to the process used to produce the software. To build high-quality software, organizations have to improve their production processes continuously. It is not required that an assessment instrument should take any particular form or format. It can be, for example, a paper-based instrument containing forms, questionnaires or checklists, or it can be, for example, a computer-based instrument such as a spreadsheet, a data base system or an integrated CASE tool. Regardless of the form of the assessment instrument, its main objective is to help an assessor to perform an assessment in a consistent and repeatable manner, reducing assessor subjectivity and ensuring the validity, usability and comparability of the assessment results.

To reach this goal, an assessment instrument should be made according to the instructions defined. The ultimate goal of software engineering is to find methods for developing high quality software products at reasonable cost. As computers are being used in more and more critical areas of the industry, the quality of software becomes a key factor of business success and human safety. Two approaches can be followed to ensure software quality. One is focused on a direct specification and evaluation of the quality of software product, while the other is focused on assuring high quality of the process by which the product is developed.

The software industry is currently entering a period of maturity, in which particular informal approaches are specified more precisely and are supported by the appropriate standards. Quality characteristics of software products are defined in ISO/IEC (International Organization for Standardization/International Electrotechnical Commission) 9126 [1]. For each characteristic, a set of attributes which can be measured is determined. Such a definition helps in evaluating the quality of software, but gives no guidance on how to construct a high quality software product. The requirements for a quality management system are defined in ISO 9001 [2]. All the requirements are intended for application within a software process in order to enhance the customer satisfaction, which is considered the primary measure of the software product quality

1.1 Statement of the Problem

The following problems were identified:

  1. Lack of secure and reliable commercial software.
  2. Software vulnerabilities can compromise customer data, disrupt business services, and jeopardize trust. Therefore, customers require that software be developed in a way that minimizes the number of vulnerabilities, and customers expect suppliers to have appropriate update mechanisms for use when vulnerabilities emerge.
  • Software failures contribute to marketplace confusion and the erosion of trust between supplier and customer.

To gain the necessary confidence in acquired software, customers need a method for assessing the security of the software, including the impact the software may have on the organization’s risk posture. A process-based assessment of a supplier’s software assurance practices can deliver this confidence, empowering customers to better manage risk.

1.2 Aim and Objectives of the Study

The aim of the study is to develop an intelligent assistant system for software assessment. The following are the specific objectives of the study:

  1. To develop an intelligent system that can be used to assess software quality.
  2. To design a system that will be able to store vital information of software assessments performed.
  • To develop a system that will enable the user to determine software effectiveness.

1.3 Scope of the Study

This study is focused on design and Implementation of an intelligent assistant system for software assessment a case study of Akwa Ibom state polytechnic digital center, Ikot Osurua. It is limited to the capturing of the weighted sum of software features and the determination of the best software option based on the total weight of its features. Assessment is based on three different criteria categories which are: The vendor, hardware/ software requirements and cost/benefits of the software system.

1.4 Significance of the Study

The study is significant in the following ways:

  1. it will help institutions and organizations assess the performance level of a software product.
  2. It will help organizations to select the best performing software based on their standard for assessment.
  • It will aid in the easy management of software assessment information.
  1. The study will also serve as a useful reference material to other researchers seeking for information concerning the subject.
  • Organization of the Research

This research work is organized into five chapters.

Chapter one is concerned with the introduction of the research study and it presents the preliminaries, theoretical background, statement of the problem, aim and objectives of the study, significance of the study, scope of the study, organization of the research and definition of terms.

Chapter two focuses on the literature review, the contributions of other scholars on the subject matter is discussed.

Chapter three is concerned with the system analysis and design. It analyzes the present system to identify the problems and provides information on the advantages and disadvantages of the proposed system. The system design is also presented in this chapter.

Chapter four presents the system implementation and documentation. The choice of programming language, analysis of modules, choice of programming language and system requirements for implementation.

Chapter five focuses on the summary, conclusion and recommendations are provided in this chapter based on the study carried out.

1.6 Definition of Terms

Intelligent: Characterized by thoughtful interaction

Assessment: To carry out an evaluation to determine the value of something or the level of performance.

Performance: The amount of useful work performed by a system in relation to the time and resources used


Blazingprojects Mobile App

πŸ“š Over 50,000 Project Materials
πŸ“± 100% Offline: No internet needed
πŸ“ Over 98 Departments
πŸ” Software coding and Machine construction
πŸŽ“ Postgraduate/Undergraduate Research works
πŸ“₯ Instant Whatsapp/Email Delivery

Blazingprojects App

Related Research

Computer Science. 4 min read

Predicting Disease Outbreaks Using Machine Learning and Data Analysis...

The project topic, "Predicting Disease Outbreaks Using Machine Learning and Data Analysis," focuses on utilizing advanced computational techniques to ...

BP
Blazingprojects
Read more β†’
Computer Science. 4 min read

Implementation of a Real-Time Facial Recognition System using Deep Learning Techniqu...

The project on "Implementation of a Real-Time Facial Recognition System using Deep Learning Techniques" aims to develop a sophisticated system that ca...

BP
Blazingprojects
Read more β†’
Computer Science. 4 min read

Applying Machine Learning for Network Intrusion Detection...

The project topic "Applying Machine Learning for Network Intrusion Detection" focuses on utilizing machine learning algorithms to enhance the detectio...

BP
Blazingprojects
Read more β†’
Computer Science. 2 min read

Analyzing and Improving Machine Learning Model Performance Using Explainable AI Tech...

The project topic "Analyzing and Improving Machine Learning Model Performance Using Explainable AI Techniques" focuses on enhancing the effectiveness ...

BP
Blazingprojects
Read more β†’
Computer Science. 2 min read

Applying Machine Learning Algorithms for Predicting Stock Market Trends...

The project topic "Applying Machine Learning Algorithms for Predicting Stock Market Trends" revolves around the application of cutting-edge machine le...

BP
Blazingprojects
Read more β†’
Computer Science. 4 min read

Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems...

The project topic, "Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems," focuses on the integration of machine learn...

BP
Blazingprojects
Read more β†’
Computer Science. 4 min read

Anomaly Detection in Internet of Things (IoT) Networks using Machine Learning Algori...

Anomaly detection in Internet of Things (IoT) networks using machine learning algorithms is a critical research area that aims to enhance the security and effic...

BP
Blazingprojects
Read more β†’
Computer Science. 4 min read

Anomaly Detection in Network Traffic Using Machine Learning Algorithms...

Anomaly detection in network traffic using machine learning algorithms is a crucial aspect of cybersecurity that aims to identify unusual patterns or behaviors ...

BP
Blazingprojects
Read more β†’
Computer Science. 2 min read

Predictive maintenance using machine learning algorithms...

Predictive maintenance is a proactive maintenance strategy that aims to predict equipment failures before they occur, thereby reducing downtime and maintenance ...

BP
Blazingprojects
Read more β†’
WhatsApp Click here to chat with us