Money Management App for Expense Planning Based on Flutter

A Flutter money management app. Users set income and expenses by category, plan budgets and track spending to build a saving habit.

The user installs the app and signs in; credentials are checked against Firebase Authentication or the app's own API. Screens built in XML (or Jetpack Compose) collect the user's input and show results in lists and cards. Data is stored in Firebase Realtime Database / Firestore or a local SQLite database and synced when the phone is online. Notifications and background services alert the user to updates, and an admin side manages the records.

An Activity moves through onCreate, onStart, onResume (running), onPause, onStop and onDestroy. Saving state in onPause or onSaveInstanceState prevents data loss when the user switches apps or rotates the screen.

Firebase gives a hosted real-time database, authentication and push notifications without running our own server. SQLite stores data locally on the phone and works offline. We chose based on whether data must be shared between users.

UIKit is the older imperative framework where views are created and updated in code or storyboards. SwiftUI is declarative: you describe the UI for a given state and the framework updates it when the state changes.

Packet delivery ratio, end-to-end delay, throughput, packet loss and energy consumption, compared between the existing protocol and the proposed one.

Proactive protocols (DSDV, OLSR) keep routes to all nodes updated at all times. Reactive protocols (AODV, DSR) find a route only when data needs to be sent, which saves overhead in mobile networks.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.