Software Requirements Specification (SRS) for Web Applications: A Practical Template

The Indispensable Role of a Software Requirements Specification (SRS)

Any web software project MUST start with a Requirements Document, often called a Software Requirements Specification (SRS). This document serves as the foundational source of truth, guiding engineering decisions and forming the basis for tasks in the initial development stages (namely 0. Plan and 1. Create).

As highlighted in MIT's software engineering courses, a clear SRS prevents issues, ensuring all stakeholders have a shared understanding of the product, its purpose, features, and constraints.

This SRS Template aims to maintain compliance with ISO/IEC/IEEE 29148:2018 and includes a technical appendix.

Compact SRS Template

### 1. Introduction
#### 1.1 Purpose
#### 1.2 Scope
#### 1.3 System Context
##### C4 Model - Level 1: System Context
##### UML - Use Case Diagram (High Level)

### 2. Functional Requirements
#### 2.1 User Interface (UI)
#### 2.2 Natural Language Processing (NLP)
#### 2.3 Knowledge Retrieval
#### 2.4 Personalization
#### Diagram: Use Cases
##### UML - Use Case Diagram (Detailed)
##### C4 - Conceptual Component Interaction (Illustrative)

### 3. Non-Functional Requirements
#### 3.1 Performance
#### 3.2 Security
#### 3.3 Scalability
#### 3.4 Compliance
#### 3.5 Availability
#### 3.6 Usability

### 4. System Interfaces
#### 4.1 API Integration (if applicable)
#### Diagram: System Architecture / Container View
##### C4 Model - Level 2: Container Diagram
##### UML - Deployment Diagram (Conceptual)

### 5. User Characteristics
#### 5.1 User Roles
#### 5.2 Technical Proficiency

### 6. Constraints
#### 6.1 Technological Constraints
#### 6.2 Budget Constraints
#### 6.3 Time Constraints

### 7. Assumptions and Dependencies
#### 7.1 Assumptions
#### 7.2 Dependencies

### 8. User Interfaces (UI) / User Experience (UX)
#### 8.1 Wireframes / Mockups (Conceptual)
(Placeholder for links to or embedded low-fidelity designs)
#### 8.2 Key User Scenarios / User Flows
(Placeholder for descriptions or diagrams of key user journeys)

### 9. Technical Memorandum (Optional Appendix)
#### 9.1 Further Diagram Details (e.g., C4 Level 3, UML Sequence/Activity Diagrams)
##### C4 Model - Level 3: Component Diagram (Example: API Layer)
##### UML - Sequence Diagram (Example: User Query Processing)
##### UML - Activity Diagram (Example: New User Registration)
#### 9.2 Data Model (Conceptual)
(Placeholder for ERD or conceptual data structures)
#### 9.3 Glossary
(Definitions of key terms and acronyms used in the SRS)

SRS Template: AI-Powered Conversational Agent (Example: ChatGPT)

This template uses a hypothetical AI-powered conversational agent, "ChatGPT," as an example to illustrate the various sections.

1. Introduction

1.1 Purpose

To define the functional, non-functional, and interface requirements for "ChatGPT," an AI-powered conversational agent designed for natural language interactions. This document is intended for developers, testers, project managers, and stakeholders.

1.2 Scope

The scope of this project covers the development of a web-based interface for ChatGPT, enabling users to engage in text-based conversations. The system will process natural language queries, retrieve information from a vast knowledge base, and generate human-like responses. It will also offer personalization features based on user context.

1.3 System Context

The System Context Diagram provides a high-level overview of the system and its interactions with users and other external systems.

C4 Model - Level 1: System Context

@startuml
!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Context.puml

Person(user, "User", "A person interacting with the AI agent.")
System(chat_gpt, "ChatGPT", "AI-powered conversational agent providing information and assistance.")
System_Ext(knowledge_base, "Knowledge Base", "External or internal vast repository of information.")

Rel(user, chat_gpt, "Interacts with, Asks questions, Receives answers")
Rel(chat_gpt, knowledge_base, "Retrieves information from, Updates (potentially)")

SHOW_LEGEND()
@enduml

@startuml
left to right direction
actor "User" as user
rectangle "ChatGPT System" {
  usecase "Interact with AI" as UC1
  usecase "Access Knowledge Base" as UC2
}
user -- UC1
UC1 ..> UC2 : include
note right of UC2 : External System
@enduml

2. Functional Requirements

2.1 User Interface (UI)

FR2.1.1: The system shall provide a clean, intuitive, text-based chat interface. FR2.1.2: The interface shall display the conversation history during a session. FR2.1.3: Users shall be able to input text queries easily.

2.2 Natural Language Processing (NLP)

FR2.2.1: The system shall understand and interpret user queries in natural language (English, initially). FR2.2.2: The system shall generate human-like, coherent, and contextually relevant text responses. FR2.2.3: The system should handle common typos and grammatical errors gracefully.

2.3 Knowledge Retrieval

FR2.3.1: The system shall access and utilize a vast, up-to-date knowledge base to answer queries. FR2.3.2: The system shall be able to synthesize information from multiple sources within the knowledge base if required.

2.4 Personalization

FR2.4.1: The system should adapt its responses based on the ongoing conversation context (e.g., previous questions asked by the user in the current session). FR2.4.2: (Optional/Future) The system may allow users to set preferences for response style or information depth.

Diagram: Use Cases

UML - Use Case Diagram (Detailed)

@startuml
left to right direction
actor User

rectangle "ChatGPT System" {
  User -- (Input Query)
  (Input Query) .> (Process Query) : extends
  (Process Query) ..> (Access Knowledge Base) : include
  (Process Query) ..> (Generate Response) : include
  (Generate Response) --> (Display Response)
  User -- (Receive Response) : (Display Response) <..
  User -- (Refine Query)
}
@enduml

C4 - Conceptual Component Interaction (Illustrative)

@startuml
!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Component.puml

Container_Boundary(web_app, "Web Application") {
    Component(ui_handler, "UI Handler", "Web Component", "Handles user input and displays responses")
    Component(query_processor, "Query Processor", "Service", "Processes user queries, interacts with NLP")
    Component(nlp_engine_interface, "NLP Engine Interface", "Module", "Interface to the core NLP capabilities")
}
System_Ext(nlp_engine, "NLP Engine", "Core AI model for understanding and generation")
System_Ext(knowledge_base, "Knowledge Base", "Data Store")

Rel(ui_handler, query_processor, "Sends user query")
Rel(query_processor, nlp_engine_interface, "Forwards query for NLP processing")
Rel(nlp_engine_interface, nlp_engine, "Interacts with NLP Engine")
Rel(nlp_engine, knowledge_base, "Accesses for information")
Rel(nlp_engine_interface, query_processor, "Returns processed information/response")
Rel(query_processor, ui_handler, "Sends generated response for display")

SHOW_LEGEND()
@enduml

3. Non-Functional Requirements

3.1 Performance

NFR3.1.1: Average response time for user queries shall be less than 5 seconds under normal load conditions. NFR3.1.2: The system shall support up to 1,000 concurrent users without significant degradation in performance. (Example value, adjust as needed)

3.2 Security

NFR3.2.1: All user data (e.g., conversation logs, if stored) shall be protected using industry-standard encryption mechanisms both in transit (TLS 1.2+) and at rest (AES-256 or equivalent). NFR3.2.2: The system shall implement measures to prevent common web vulnerabilities (e.g., XSS, CSRF). NFR3.2.3: User inputs should be sanitized to prevent injection attacks against the backend systems.

3.3 Scalability

NFR3.3.1: The system architecture shall be designed to handle a 10x increase in concurrent users (up to 10,000) with horizontal scaling of application and NLP processing resources. NFR3.3.2: The knowledge base interface should be scalable to accommodate growing data volumes.

3.4 Compliance

NFR3.4.1: The system shall adhere to relevant AI ethics guidelines regarding bias, fairness, and transparency (specify which guidelines if applicable). NFR3.4.2: If personal data is processed, the system must comply with relevant data protection regulations (e.g., GDPR, CCPA).

3.5 Availability

NFR3.5.1: The system shall have an uptime of 99.9% (excluding scheduled maintenance).

3.6 Usability

NFR3.6.1: The user interface should be intuitive and require minimal training for new users. NFR3.6.2: Error messages shall be clear and provide guidance to the user.

4. System Interfaces

4.1 API Integration (if applicable)

SI4.1.1: The system shall provide a RESTful API for third-party integrations (e.g., embedding ChatGPT in other applications). SI4.1.2: The API shall be secured using token-based authentication (e.g., OAuth 2.0). SI4.1.3: API documentation shall be provided (e.g., Swagger/OpenAPI).

Diagram: System Architecture / Container View

This diagram shows the major building blocks of the system.

C4 Model - Level 2: Container Diagram

@startuml
!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Container.puml

Person(user, "User", "Interacts with the system.")

System_Boundary(chatgpt_system, "ChatGPT System") {
    Container(frontend, "Web Application", "JavaScript, React/Next.js", "Provides the user interface for chat.")
    Container(api_layer, "API Layer", "Node.js, Python/Flask", "Handles incoming requests, orchestrates backend services.")
    ContainerDb(session_db, "Session Database", "Redis/Memcached", "Stores temporary session data, conversation context.")
    Container(nlp_engine_service, "NLP Engine Service", "Python, AI Frameworks", "Core NLP processing, query understanding, response generation.")
    ContainerDb(knowledge_base_proxy, "Knowledge Base Proxy/Cache", "Service + Cache", "Manages access to and caches data from the Knowledge Base.")
}

System_Ext(knowledge_base, "Knowledge Base", "External Data Repository")
System_Ext(identity_provider, "Identity Provider", "OAuth2/SSO Service (Optional)")


Rel(user, frontend, "Uses", "HTTPS")
Rel_R(frontend, api_layer, "Sends queries to / Receives responses from", "HTTPS/JSON API")
Rel_R(api_layer, session_db, "Stores/Retrieves session data", "TCP/IP")
Rel_R(api_layer, nlp_engine_service, "Forwards queries / Gets NLP results", "gRPC/HTTPS")
Rel_R(nlp_engine_service, knowledge_base_proxy, "Retrieves information", "Internal Protocol")
Rel_R(knowledge_base_proxy, knowledge_base, "Accesses", "API/DB Connection")

' Optional Authentication Flow
' Rel(frontend, identity_provider, "Authenticates via (if login is required)")
' Rel(api_layer, identity_provider, "Validates tokens via (if login is required)")


SHOW_LEGEND()
@enduml

UML - Deployment Diagram (Conceptual)

@startuml
node "User's Browser" as UserBrowser
artifact "WebApp.js" as WebAppJS

node "Web Server Cluster" {
  artifact "FrontendApp.war" as FrontendApp
  node "Application Server" as AppServer {
    artifact "APIService.jar" as APIService
  }
  node "NLP Processing Cluster" {
      artifact "NLPEngine.py" as NLPEngine
  }
  database "SessionDB" {
    artifact "SessionData"
  }
  node "KB Proxy Server" {
      artifact "KBProxyService.jar" as KBProxy
  }
}

node "External Knowledge Base" as ExtKB

UserBrowser ..> FrontendApp : HTTPs
FrontendApp ..> APIService : HTTPs/JSON
APIService ..> NLPEngine : gRPC/HTTPs
APIService ..> SessionDB : TCP/IP
NLPEngine ..> KBProxy : Internal
KBProxy ..> ExtKB : API/DB Conn

@enduml

5. User Characteristics

UC5.1: Target users are the general public with varying levels of technical proficiency. UC5.2: Users are expected to have access to a modern web browser and a stable internet connection. UC5.3: No specialized knowledge should be required to use the basic functionalities of the system.

6. Constraints

C6.1: Initial input/output is restricted to text only. C6.2: The system will not have long-term memory of individual users across different sessions unless a user explicitly logs in and personalization features are enabled. C6.3: Development must use approved technology stacks (e.g., React/Next.js for frontend, Python/Node.js for backend - specify if known). C6.4: The project timeline is X months, and the budget is Y (if applicable).

7. Assumptions and Dependencies

Assumptions

A7.1: Users have reliable internet connectivity. A7.2: The external knowledge base API (if applicable) will be available and performant. A7.3: Sufficient computational resources will be available for training (if applicable) and deploying the AI model.

Dependencies

D7.1: The system depends on the availability and accuracy of the underlying AI model (e.g., GPT). D7.2: The system depends on the continuous availability of the Knowledge Base. D7.3: Regular updates and fine-tuning of the AI model will be necessary to maintain and improve performance and relevance.

Diagram: Dependency Relationships (Conceptual)

C4 - Context with Dependencies Highlighted

@startuml
!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Context.puml

Person(user, "User")
System(chat_gpt, "ChatGPT System", "Our AI Conversational Agent")

System_Ext(internet_connectivity, "Internet Connectivity", "External Dependency")
System_Ext(ai_model_provider, "AI Model Provider", "External Dependency (e.g., OpenAI API or Self-hosted Model Infrastructure)")
System_Ext(knowledge_base, "Knowledge Base", "External Data Source")
System_Ext(model_update_pipeline, "Model Update Pipeline", "Internal/External Process for AI model updates")

Rel(user, chat_gpt, "Requires", "Relies on")
Rel(chat_gpt, internet_connectivity, "Requires", "For user access and potentially external API calls")
Rel(chat_gpt, ai_model_provider, "Depends on", "For core NLP capabilities")
Rel(chat_gpt, knowledge_base, "Depends on", "For information retrieval")
Rel(chat_gpt, model_update_pipeline, "Depends on", "For ongoing model improvements")

SHOW_LEGEND()
@enduml

UML - Package Diagram (Conceptual Dependencies)

@startuml
package "ChatGPT System" {
  [ChatGPT Core]
}
package "External Dependencies" {
  [Internet Connectivity]
  [AI Model Services]
  [Knowledge Base Service]
  [Model Update Process]
}

[ChatGPT Core] ..> [Internet Connectivity] : uses
[ChatGPT Core] ..> [AI Model Services] : uses
[ChatGPT Core] ..> [Knowledge Base Service] : uses
[ChatGPT Core] ..> [Model Update Process] : relies on
@enduml

8. User Interfaces (UI Details)

8.1 Web Interface

UI8.1.1: A main chat window shall display the conversation flow. UI8.1.2: A text input field shall be provided for users to type their queries. UI8.1.3: A "Send" button or "Enter" key functionality shall submit the query. UI8.1.4: The design shall be responsive, adapting to various screen sizes (desktop, tablet, mobile). UI8.1.5: (Optional) Options to clear conversation, copy text, or provide feedback on responses.

8.2 Accessibility

UI8.2.1: The interface shall be navigable using a keyboard. UI8.2.2: The interface shall be compatible with common screen readers (e.g., NVDA, JAWS), adhering to WCAG 2.1 Level AA guidelines. UI8.2.3: Sufficient color contrast shall be used for text and UI elements.

Diagram: User Interface Mockup / Wireframe Placeholder

It's highly recommended to include visual mockups or links to wireframes here. For this text-based template, we'll describe it.

Description of User Interface Mockup: A simplified chat interface:

Header: Title "ChatGPT" Main Area (Conversation Log): Alternating user queries and AI responses. User queries aligned to one side (e.g., right), AI responses to the other (e.g., left). Timestamps optional. Input Area (Footer): A text input box spanning most of the width, with a "Send" button to its right. (Placeholder for an actual image or embedded wireframe) [User Interface Mockup: Simplified chat interface. Consider embedding an image or using a tool to generate a textual representation if possible, e.g., ASCII art for extreme simplicity, or link to a design tool like Figma/Balsamiq.]

Appendix A: Technical Memorandum

This section provides deeper technical details, primarily for the engineering team.

A.1 AI Model

TM.A.1.1: The core conversational AI will be based on a Generative Pre-trained Transformer (GPT) architecture (e.g., GPT-3.5, GPT-4, or a custom-trained variant). TM.A.1.2: Specify model version or source if applicable.

A.2 Training Data (if applicable for custom models)

TM.A.2.1: The model is pre-trained on diverse internet text sources. TM.A.2.2: If fine-tuning is performed, specify the datasets used and the objectives of fine-tuning (e.g., domain adaptation, safety alignment).

A.3 Deployment

TM.A.3.1: The system will be deployed on a cloud-based infrastructure (e.g., AWS, Azure, GCP). TM.A.3.2: Key services (API Layer, NLP Engine Service) will be containerized (e.g., using Docker) and orchestrated (e.g., using Kubernetes).

A.4 Continuous Learning / Model Updates

TM.A.4.1: A strategy for regular model updates and fine-tuning shall be in place to incorporate new knowledge and improve performance. TM.A.4.2: Mechanisms for monitoring model performance and identifying areas for improvement should be implemented.

A.5 Ethical Considerations & Bias Mitigation

TM.A.5.1: Implement content filtering mechanisms to reduce the generation of harmful, inappropriate, or biased content. TM.A.5.2: Regularly audit the model's responses for potential biases and develop strategies for mitigation. TM.A.5.3: Provide users with clear disclaimers about the AI's limitations and potential for errors.

Diagram: C4 Model - Level 3: Component Diagram (Example for API Layer)

This diagram zooms into one of the containers defined in the Level 2 diagram, showing its internal components.

@startuml
!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Component.puml

Container_Boundary(api_layer, "API Layer") {
    Component(request_handler, "Request Handler", "Controller/Router", "Receives HTTP requests, validates input, routes to appropriate service.")
    Component(auth_service, "Authentication Service", "Middleware/Service", "Verifies user credentials/tokens.")
    Component(orchestration_service, "Orchestration Service", "Service", "Coordinates calls to NLP Engine, Session DB, and other backend services.")
    Component(session_manager, "Session Manager", "Service", "Manages user session data and conversation context via Session DB.")
    Component(nlp_client, "NLP Service Client", "Client Library", "Handles communication with the NLP Engine Service.")
    Component(response_formatter, "Response Formatter", "Utility", "Formats NLP results into the API response structure.")
}

' External interactions for context
Container(frontend, "Web Application", "JavaScript, React/Next.js", "External - Sends requests")
ContainerDb(session_db, "Session Database", "Redis/Memcached", "External - Stores session data")
Container(nlp_engine_service, "NLP Engine Service", "Python, AI Frameworks", "External - Provides NLP capabilities")


Rel(frontend, request_handler, "Sends API Requests to", "HTTPS/JSON")
Rel(request_handler, auth_service, "Uses for authentication")
Rel(request_handler, orchestration_service, "Routes validated requests to")
Rel(orchestration_service, session_manager, "Uses for session context")
Rel(session_manager, session_db, "Reads/Writes to")
Rel(orchestration_service, nlp_client, "Sends queries via")
Rel(nlp_client, nlp_engine_service, "Communicates with")
Rel(orchestration_service, response_formatter, "Uses to format response")
Rel(response_formatter, request_handler, "Returns formatted response to")


SHOW_LEGEND()
@enduml

This C4 Component diagram shows the internal components of the 'API Layer' container, such as 'Request Handler', 'Authentication Service', 'Orchestration Service', 'Session Manager', 'NLP Service Client', and 'Response Formatter', and how they interact.