Systems Development Life Cycle (SDLC) Overview

Systems Life Cycle (SLDC)

• Conceptual model for project management
• Stages from initial study to maintenance

7 Phases of SDLC

• Planning
• Analysis/Feasibility
• Design
• Development
• Testing
• Implementation
• Maintenance

6 Basic Types of SDLC Models

Waterfall Model

• Linear, structured, sequential approach
• Separate project plans for each stage
• Suitable for projects with well-defined goals and timelines
• No feedback loop

Iterative Model

• Improvements and changes possible at each stage
• Suitable for repeating development cycles
• Early feedback facilitates adjustments
• Easier to incorporate changes

Spiral Model

• Combines waterfall and iterative approaches
• Uses repeated cycles or loops
• Four phases: Planning, Risk Analysis, Development, Evaluation

V-Model

• Variation of waterfall model
• Each phase has a corresponding testing phase
• Verification Phase: Business requirements, design, architecture, module design, coding
• Validation Phase: Unit, integration, system, acceptance testing

Big Bang Model

• Simplest model
• Combines all efforts, time, and resources
• Integrate individual modules for a unified overview
• Use for projects with clearly defined requirements and expectations
• Not suitable for projects with unclear user expectations or requirements

Agile Model

• Popular model emphasizing collaboration
• Focuses on small, iterative changes
• Includes phases: Plan, design, develop, test, deploy, review, launch
• Utilizes methods like Scrum, Crystal, Kanban, Extreme Programming, DSDM, Lean software development, Feature-Driven Development

Benefits of System Life Cycle

• Enhanced product quality
• Improved project management
• Effective risk management
• Improved communication and collaboration
• Efficient resource utilization
• Flexibility and adaptability

Systems Analysis & Design (SAD)

• Structured approach for IS development, maintenance, improvement
• System Analysts plan, develop, and maintain ISs
• Benefits include: Improved efficiency, better decision-making, cost savings, alignment to business goals

Tools and Techniques in Systems Analysis

Data Flow Diagrams (DFDs)

• Visual representation of data movement in a system
• Illustrates problem analysis, abstraction, hierarchy, data flow, modularity

Entity-Relationship Diagrams (ERDs)

• Flowchart depicting relationships between entities in a system
• Used in relational database design and debugging

Unified Modeling Language (UML)

• Standard notation for object-oriented analysis and design
• Examples include Class diagrams, Use case diagrams, Sequence diagrams, Activity diagrams

Database Design in SAD

• Conceptual, logical, and physical design stages

System Security and Controls

• Authentication verifies user access (passwords, biometrics, two-factor, token-based)
• Authorization restricts access based on roles (RBAC, ABAC)
• Encryption protects data confidentiality (symmetric, asymmetric)
• Auditing tracks system activities and potential intrusions

Disaster Recovery

• Plans for system restoration after catastrophic events
• Include backup systems, recovery plans, redundancy

Compliance

• Adherence to regulations (GDPR, HIPAA, PCI DSS)

Ethical Considerations in SAD

• User trust and privacy (protection of user data, unauthorized access prevention)
• Compliance with laws and regulations (Avoiding potential legal issues, maintaining reputation)
• Stakeholder interests and impact (Balancing diverse needs)
• Unintended effects on society, individuals, and environment
• Professional integrity and reputation (High ethical standards)

Emerging Trends in SAD

• Cloud computing (scalability, cost-efficiency, collaboration)
• Internet of Things (integration of devices, automation, user interfaces)
• AI and Machine learning (enhancing capabilities, automating tasks)
• Big Data (analyzing large volumes)

Structured Analysis Methodology

• Uses graphical diagrams to depict system specifications
• Attributes include graphical, hierarchical, logical, and modular systems

Data Modeling

• Creating visual representation of an organization's data
• Benefits include simplified data, reduced redundancy, improved data integrity, data sharing, and improved data reporting

Input and Output Design

• Input Design: Converting raw data to computer format
• Objectives: Design data entry and capture procedures, reduce input volume
• Output Design: Defining, formatting, and delivering generated information
• Objectives: Provide accurate information, meet user requirements, aid decision-making, support multiple formats

SA Major Processes

• Study current business environment
• Modeling old and new logical systems
• Modeling new physical environments
• Evaluating alternatives
• Selecting best design
• Creating structure specifications

Techniques from 3 Orthogonal Views (SA Process)

• Functional View (Defines work done and data flow)
• Data View (Entity-Relationship diagrams)
• Dynamic View (Transition diagrams, conditions)

Data Modeling Benefits

• Simplification
• Avoidance of redundancy
• Improved data integrity
• Data sharing
• Improved data reports

Physical and Logical Data

• Physical Data: How data model is built in the database
• Logical Data: Detailed description of data elements and connections

This quiz covers the Systems Development Life Cycle (SDLC), detailing its phases from planning to maintenance. It also explores various SDLC models such as Waterfall, Iterative, Spiral, and V-Model, emphasizing their unique features and suitable project scenarios.