Project Overview

The MindArch Project​ approaches the completion of its Alpha development phase with Day 9 (December 20, 2024) focused on implementing advanced AI capabilities and ensuring robust system connectivity. Today's work represents the final major feature implementations before the project demonstration.

Today's Sprint Goals & Completion Status

• ✅ Complete AI conversation functionality

• ✅ Implement automatic reconnection mechanism

• ✅ Conduct comprehensive functional testing

• ✅ Fix known bugs and issues

Project Progress Tracking

Burn-down Chart Analysis

Remaining Task Points
100 |●
 90 |  ●
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 50 |          ●
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  0 |                    ●
    1  2  3  4  5  6  7  8  9  10 (Days)

Current Progress: 95% complete, 5% remaining

Technical Implementation Details

1. AI Conversation System

1.1 Architecture Design

Core Files: chatpage.cpp, chatpage.h

Data Structures:

// AI chat request structure
struct AI_CHAT_REQ {
    char user_id[32];        // User ID
    char message[2048];      // User message content
    char session_id[64];     // Session ID (for context maintenance)
};

// AI response structure
struct AI_CHAT_RESP {
    int status_code;         // 0=success, 1=failure
    char ai_reply[4096];     // AI response content
    char session_id[64];     // Session ID
    int tokens_used;         // Token consumption count
};

1.2 Core Implementation

Message Processing Logic:

void ChatPage::sendAIMessage(const QString& message)
{
    if (message.trimmed().isEmpty()) return;
    
    // Display user message
    addMessageBubble(message, true, QDateTime::currentDateTime());
    
    // Show "AI is thinking..." indicator
    QLabel* thinkingLabel = new QLabel("AI is thinking...");
    thinkingLabel->setStyleSheet(thinkingStyleSheet);
    ui->messageLayout->addWidget(thinkingLabel);
    
    // Construct AI request
    AI_CHAT_REQ req;
    memset(&req, 0, sizeof(req));
    
    strncpy(req.user_id, m_session.userId.toUtf8().constData(),
            sizeof(req.user_id) - 1);
    strncpy(req.message, message.toUtf8().constData(),
            sizeof(req.message) - 1);
    strncpy(req.session_id, m_aiSessionId.toUtf8().constData(),
            sizeof(req.session_id) - 1);
    
    // Server communication and response handling
    QByteArray reqBody(reinterpret_cast<const char*>(&req), sizeof(req));
    HEAD respHead;
    QByteArray respBody = FrontClient::instance()
        .sendAndReceive(AI_CHAT, reqBody, &respHead);
    
    // Remove thinking indicator
    delete thinkingLabel;
    
    if (respBody.size() >= sizeof(AI_CHAT_RESP)) {
        AI_CHAT_RESP* resp = reinterpret_cast<AI_CHAT_RESP*>(respBody.data());
        
        if (resp->status_code == 0) {
            m_aiSessionId = QString::fromUtf8(resp->session_id);
            QString aiReply = QString::fromUtf8(resp->ai_reply);
            addMessageBubble(aiReply, false, QDateTime::currentDateTime());
        }
    }
}

1.3 User Interface Components

Message Bubble Styling:

void ChatPage::addMessageBubble(const QString& message, 
                                bool isUser, 
                                const QDateTime& time)
{
    QWidget* bubbleWidget = new QWidget();
    QHBoxLayout* layout = new QHBoxLayout(bubbleWidget);
    
    QLabel* bubble = new QLabel(message);
    bubble->setWordWrap(true);
    bubble->setMaximumWidth(400);
    bubble->setTextInteractionFlags(Qt::TextSelectableByMouse);
    
    if (isUser) {
        // User messages: right-aligned, green background
        bubble->setStyleSheet(userBubbleStyle);
        layout->addStretch();
        layout->addWidget(bubble);
    } else {
        // AI messages: left-aligned, gray background
        bubble->setStyleSheet(aiBubbleStyle);
        QLabel* avatar = new QLabel();
        avatar->setPixmap(QPixmap(":/image/image/ai_avatar.png")
                         .scaled(40, 40, Qt::KeepAspectRatio, 
                                Qt::SmoothTransformation));
        layout->addWidget(avatar);
        layout->addWidget(bubble);
        layout->addStretch();
    }
    
    ui->messageLayout->addWidget(bubbleWidget);
}

2. Connection Resilience System

2.1 Heartbeat Monitoring Mechanism

Core Files: frontclient.cpp, frontclient.h

Heartbeat Implementation:

void FrontClient::setupHeartbeat()
{
    m_heartbeatTimer = new QTimer(this);
    m_heartbeatTimer->setInterval(30000);  // 30-second intervals
    
    connect(m_heartbeatTimer, &QTimer::timeout, 
            this, &FrontClient::sendHeartbeat);
    
    m_heartbeatTimer->start();
}

void FrontClient::sendHeartbeat()
{
    if (!m_socket || m_socket->state() != QAbstractSocket::ConnectedState) {
        qDebug() << "Connection lost, attempting reconnect...";
        attemptReconnect();
        return;
    }
    
    // Send heartbeat packet
    HEARTBEAT_REQ req;
    memset(&req, 0, sizeof(req));
    req.timestamp = QDateTime::currentSecsSinceEpoch();
    
    QByteArray reqBody(reinterpret_cast<const char*>(&req), sizeof(req));
    
    // Package and send heartbeat
    HEAD head;
    memset(&head, 0, sizeof(head));
    head.type = HEARTBEAT;
    head.length = sizeof(req);
    
    QByteArray packet;
    packet.append(reinterpret_cast<const char*>(&head), sizeof(head));
    packet.append(reqBody);
    
    qint64 written = m_socket->write(packet);
    if (written == -1) {
        attemptReconnect();
    }
}

2.2 Automatic Reconnection Strategy

Exponential Backoff Implementation:

void FrontClient::doReconnect()
{
    if (m_reconnectAttempts >= MAX_RECONNECT_ATTEMPTS) {
        qDebug() << "Max reconnect attempts reached";
        m_isReconnecting = false;
        emit connectionLost();
        return;
    }
    
    m_reconnectAttempts++;
    
    // Clean up old connections
    if (m_socket) {
        m_socket->disconnectFromHost();
        m_socket->deleteLater();
        m_socket = nullptr;
    }
    
    // Create new connection with exponential backoff
    m_socket = new QTcpSocket(this);
    
    connect(m_socket, &QTcpSocket::connected, 
            this, &FrontClient::onReconnected);
    
    m_socket->connectToHost(m_serverIp, m_serverPort);
    
    // Set timeout with exponential delay
    int delay = qMin(1000 * (1 << m_reconnectAttempts), 30000);
    QTimer::singleShot(delay, this, &FrontClient::doReconnect);
}

3. Comprehensive Testing Framework

3.1 Test Case Implementation

Login Testing Suite:

// Test Case 1: Normal login flow
void TestLogin::testNormalLogin()
{
    QString userId = "test001";
    QString password = "123456";
    
    bool result = performLogin(userId, password);
    QVERIFY(result == true);
    
    SessionData session = LocalStore::instance().loadSession();
    QCOMPARE(session.userId, userId);
    QVERIFY(!session.token.isEmpty());
}

// Test Case 2: AI conversation functionality
void TestAI::testAIChat()
{
    QString userMessage = "Hello, I'm feeling anxious";
    
    QString aiReply = sendAIMessage(userMessage);
    
    QVERIFY(!aiReply.isEmpty());
    QVERIFY(aiReply.length() > 10);
    
    // Validate AI response contains relevant keywords
    QVERIFY(aiReply.contains("anxious") || 
            aiReply.contains("understand") || 
            aiReply.contains("help"));
}

3.2 Test Results Summary

Test Module	Test Cases	Passed	Failed	Pass Rate
Login/Registration	15	15	0	100%
Friend Chat	20	20	0	100%
Daily Assessment	12	12	0	100%
Community Features	10	10	0	100%
Bookshelf Reader	8	8	0	100%
AI Conversation	10	10	0	100%
Reconnection	5	5	0	100%
Total​	80​	80​	0​	100%​

Technical Innovations

1. Context-Aware AI Conversations

Implemented session-based context maintenance allowing for coherent multi-turn conversations with the AI assistant, significantly enhancing user experience.

2. Intelligent Reconnection System

Developed exponential backoff algorithm for connection recovery, balancing quick reconnection attempts with server load considerations.

3. Real-time Connection Monitoring

30-second heartbeat mechanism provides immediate detection of connection issues with automatic recovery procedures.

Development Metrics

Metric	Day 9 Results
Code Commits	22 commits
New Lines of Code	650 lines
Test Cases Executed	80 cases
Test Pass Rate	100%
Bugs Fixed	15 issues

Interface Preview 

AI Conversation Interface

Connection Status Indicator

Challenges and Solutions

1. AI Response Consistency

Challenge: Maintaining context across conversation turns

Solution: Implemented session ID tracking with server-side context management

2. Connection Stability

Challenge: Handling various network disruption scenarios

Solution: Multi-layered reconnection strategy with user-friendly status indicators

3. Testing Comprehensiveness

Challenge: Ensuring complete test coverage for all features

Solution: Developed automated test suites with scenario-based testing approach

Quality Assurance

Testing Methodology

• Unit Testing: Individual component validation

• Integration Testing: Cross-module functionality verification

• User Acceptance Testing: Real-world scenario validation

• Performance Testing: Response time and stability under load

Code Quality Standards

• Test Coverage: 100% of new code covered

• Static Analysis: Zero critical issues reported

• Performance Metrics: AI response time under 3 seconds for 95% of requests

Team Collaboration Insights

Agile Development Practices

• Daily Stand-ups: Effective coordination across development and testing teams

• Code Reviews: Ensured implementation quality and knowledge sharing

• Continuous Integration: Automated testing and build verification

Technical Achievements

The successful implementation of AI conversation capabilities demonstrates the team's ability to integrate advanced technologies while maintaining system stability and performance.

Learning Outcomes

Technical Skill Development

1. AI Integration: Mastered conversational AI implementation patterns

2. Network Programming: Advanced understanding of TCP connection management

3. Testing Strategies: Comprehensive test automation techniques

4. Performance Optimization: Efficient resource utilization in real-time systems

Project Management Insights

The parallel development of complex features like AI chat and connection resilience required careful coordination and risk management, providing valuable experience in large-scale project execution.

Conclusion

Day 9 represents the culmination of the MindArch project's Alpha development phase, with all core functionalities successfully implemented and thoroughly tested. The AI conversation system provides intelligent mental health support, while the robust connection infrastructure ensures reliable user experience.

The 100% test pass rate across 80 test cases demonstrates the project's stability and readiness for the next phase. As we approach the final day of Alpha development, the foundation is solid for project demonstration and transition to Beta testing.