Technical Design Document

Product Overview

Purpose

The primary purpose of the 'Smart Assistant' feature in WorkEasy is to help users automate their routine tasks. This feature aims to solve the problem of time-consuming repetitive tasks, enabling users to focus on more strategic work. For example, a user can create a custom workflow that organizes emails, schedules meetings, and generates reports automatically.

Target Audience

The main users of this feature are office workers who rely heavily on digital tools for their daily activities. These users often face challenges managing their workload efficiently. The Smart Assistant targets the following personas:

• Mid-level Managers: Need to streamline their administrative tasks to focus on team management and strategic planning.
• Administrative Assistants: Seek to automate routine data entry and scheduling tasks.
• Sales Representatives: Look for ways to automate customer follow-ups and CRM updates.

Expected Outcomes

The expected outcomes from implementing the Smart Assistant feature are:

• Increased Efficiency: Reduce time spent on routine tasks by at least 30%.
• Improved Productivity: Allow users to allocate more time to high-value activities.
• Enhanced User Satisfaction: Increased adoption of WorkEasy due to enhanced capabilities.
• KPIs: Monitor average task completion time, user engagement rate, and feature adoption metrics to measure success.

Design Details

Architectural Overview

The Smart Assistant feature will be integrated into the existing WorkEasy architecture. The high-level architecture involves multiple components:

• Backend Services: Handle task execution and automation logic.
• Frontend Interface: User interface for setting up and managing automations.
• Database: Store user preferences and automation configurations.
• API Gateway: Facilitate communication between frontend and backend services.

graph TD;
A[Frontend Interface] -->|API Calls| B[API Gateway]
B --> C[Backend Services]
C --> D[Database]

Data Structures and Algorithms

We will use a tree-like data structure to represent the hierarchy of tasks. Each node represents a task, and edges represent dependencies between tasks. The Depth-First Search (DFS) algorithm will be used to traverse the tree and execute tasks in the correct order. This approach ensures that all dependencies are resolved before a task is marked as complete.

Data Structures:

class TaskNode {
  String taskId;
  String taskName;
  List<TaskNode> dependencies;
  TaskStatus status;
}

enum TaskStatus {
  PENDING,
  IN_PROGRESS,
  COMPLETED
}

System Interfaces

System Interfaces:

• API Endpoints:
  • POST /createAutomation: Create a new automation workflow.
  • GET /getAutomations: Retrieve list of user automations.
  • PUT /updateAutomation: Update existing automation.
  • DELETE /deleteAutomation: Delete an automation workflow.
• Third-Party Services: Integration with calendar services, email servers, and CRM systems.
• Internal Modules: Communication with WorkEasy's core modules for task management and notifications.

User Interfaces

User Interfaces:

• Automation Builder: A drag-and-drop interface within a new 'Automation' tab in WorkEasy. Users can add, modify, and sequence tasks.
• Notification Center: Alerts users of automation status and any needed interventions.
• Dashboard: Provides analytics and performance metrics of the automated tasks.

Hardware Interfaces

No special hardware interfaces are required as this is a purely software-based feature.

Testing Plan

Test Strategies

A combination of unit testing, integration testing, system testing, and user acceptance testing (UAT) will be employed to ensure the feature meets all quality standards:

• Unit Testing: Verify individual components like task creation, modification, and deletion functionalities.
• Integration Testing: Test the interaction between the automation builder and backend services.
• System Testing: Validate the feature works as intended within the broader WorkEasy system.
• User Acceptance Testing: Conduct sessions with a subset of users to gather feedback and ensure the feature meets user needs.

Testing Tools

• Unit Testing: JUnit for backend, Jest for frontend.
• Integration Testing: Postman for API testing.
• System Testing: Selenium for end-to-end testing.
• UAT: Use of feedback tools such as UserTesting.com.

Testing Environments

• Development Environment: Local machines for initial development and unit testing.
• Staging Environment: A cloud-based environment that mirrors production for integration and system testing.
• Production Environment: Limited rollout in the live environment for UAT and feature flagging.

Test Cases

1. Create Automation: Validate that a new automation can be created, saved, and retrieved correctly.
2. Execute Automation: Ensure tasks are executed in the correct order using the DFS algorithm.
3. Error Handling: Trigger and handle errors gracefully when dependencies are not met.
4. User Interface: Verify that the drag-and-drop interface works smoothly across different browsers and devices.

Reporting and Metrics

• Test Coverage: Target at least 90% code coverage for critical components.
• Bug Reports: Use Jira for tracking and resolving bugs.
• Performance Metrics: Monitor response times and resource usage to ensure efficiency.

Deployment Plan

Deployment Environment

• Staging Environment: Kubernetes cluster with auto-scaling for pre-production testing.
• Production Environment: High-availability cluster with disaster recovery configurations.

Deployment Tools

• CI/CD Pipeline: Jenkins for automating the build, test, and deployment processes.
• Containerization: Docker for consistent application packaging across environments.
• Version Control: Git for code management and version tracking.

Deployment Steps

1. Code Review: Ensure all code changes are peer-reviewed and approved.
2. Unit Tests: Run automated unit tests and ensure all pass.
3. Build and Package: Use Jenkins to build and package the application into Docker containers.
4. Deploy to Staging: Deploy the Docker containers to the staging environment and run integration and system tests.
5. UAT: Conduct user acceptance testing in the staging environment.
6. Deploy to Production: Once approved, deploy the application to the production environment.

Post-Deployment Verification

• Smoke Testing: Perform basic tests to ensure critical features work as expected.
• Monitoring: Use monitoring tools like Prometheus and Grafana to keep an eye on performance and errors.
• Feedback Collection: Gather user feedback to identify any issues or areas for improvement.

Continuous Deployment

• Automation Tools: Use Jenkins and Docker for automated testing and deployment.
• Scripts and Configurations: Maintain scripts for automated rollback in case of deployment failures.
• Benefits: Increased deployment frequency, reduced time to market, and quicker user feedback.