Function Point Analysis and Collaborative Study on UI Library, Open-Source DB, Cross Platform Server Environment, Object Detection Library and Cloud Computing Services
A software application can only form by the integration of different platforms, environments, languages and collaborative libraries and APIs. This collaborative study presents an extensive analysis of the software to determine the complexity of any application. The metric can only calculate the exact collaboration. A client or user can identify the accurate amount of interactions with the functions and features to find the exact complexity metric. Function point analysis will be an effective way to measure software size. Internal, external, logical queries and responses will show metric results for the detailed amalgamation. To assess the challenge, use React as UI Library, MongoDB as open-source database, Node.js as cross platform open-source server environment, Coco SSD as object detection library and Firebase as cloud computing services.
To check the appropriate incorporation and ways, develop the complex commerce website and a mechanic and car washer app that incorporate advanced features such as augmented reality (AR), real-time tracking, in-app chat functionality, and car customization options. The e-commerce website is built using React for the front-end, MongoDB for the back end, and Node.js for the back-end development. The website enables the sale of old vehicle parts and allows users to customize their cars using AR technology with the Coco SSD model. Firebase will handle image uploading. The mechanic and car washer app is built using React Native, MongoDB for the back-end, and Node.js for the back-end development. The app enables users to call a mechanic or car washer and some features such as in-app chat, real-time tracking, and payment options. One of the unique features of the project is the car customization area, which incorporates Machine Learning and Color Segmentation. To enable color segmentation, an HSV model of the user's car is created, and segmentation is performed using OpenCV, Python library. This approach allows the dynamic changing of hue and saturation of the image to colorize the segmented area. Users can customize their cars by selecting various options such as rims, color, and other modifications. Additionally, the website includes a learn-to-drive module where users can learn to drive through various videos. To enable real-time chat functionality across both the website and app, socket programming has been implemented, allowing users to communicate with each other seamlessly. A function point analysis was conducted to evaluate the size and complexity of the software development project, providing valuable insights into the effort, time, and cost required for development.
The study highlights the challenges and opportunities associated with building an e-commerce website and an app with advanced features such as AR, real-time tracking, socket programming, and car customization options. The success of the website and app depends on the effective integration of different technologies and the ability to provide seamless user experience. The study's findings will help developers and stakeholders make informed decisions, optimize resource utilization, and enhance the website and app's functionality to meet the evolving needs of the customers.
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