SDLC Guide: The 7 Key Software Development Life Cycle Phases Explained

The Software Development Life Cycle (SDLC) includes seven key phases: requirement analysis, planning, design, development, testing, deployment, and maintenance. These phases offer a structured approach to building software that meets technical and business goals. SDLC improves resource planning, reduces integration issues, and supports both traditional and modern systems, including AI-powered solutions. 

As AI becomes part of more applications, SDLC ensures proper data handling, model integration, and post-deployment updates. This guide explains each phase in detail. It also compares models like Agile, Waterfall, and Spiral, helping you choose the right approach for your project’s complexity and objectives.

Understanding SDLC: The Core Software Development Life Cycle Phases Explained

The software development life cycle phases structure complex projects into manageable stages, ensuring traceability, risk control, and compliance with performance, security, and scalability requirements. SDLC aligns with Agile, DevOps, and CI/CD pipelines to streamline releases, reduce rework, and maintain version control. 

Whether you're deploying a payment gateway or scaling a microservices architecture, these phases help you deliver production-ready software with minimal technical debt.

Ready to take the next step in your career and explore the world of software development? Whether you're looking to become a full-stack developer or specialize in AI, upGrad offers courses designed to give you the skills you need. Choose from the top-rated courses:

• Professional Certificate Program in Cloud Computing and DevOps
• AI-Powered Full-stack Development Course by IIITB
• AI-Driven Full-stack Development

Now, we will cover the 7 key phases that make up the SDLC.

The 7 Key Software Development Life Cycle Phases

There are seven crucial Software Development Life Cycle phases (SDLC), each designed to ensure that software is developed systematically and efficiently. Understanding these phases is key to managing and executing software projects successfully. Let's take a closer look at each of these phases:

Phase 1: Planning and Analysis

The first Software Development Life Cycle phase focuses on understanding the project's requirements and defining the scope. This involves detailed discussions with stakeholders, understanding their needs, and analyzing the project's feasibility. 

At this stage, the development team defines the software requirements, sets clear objectives, and outlines the expected outcomes. This phase also involves risk analysis and resource planning to ensure that the project is viable and achievable. Proper planning is essential as it forms the foundation for the software development process.

Key Deliverables:

• Project requirements document
• Feasibility study report
• Resource and timeline estimates

Also Read: Waterfall vs Agile: Difference Between Waterfall and Agile Methodologies

Phase 2: Design

The design process begins when the requirements have been precisely defined. This phase focuses on designing the software architecture based on the requirements acquired during the planning step. 

The design phase involves creating wireframes, prototypes, and detailed system designs, covering both the front-end and back-end of the application. This phase ensures that the software is scalable, secure, and can meet the desired functionality.

Designs are typically reviewed and revised to ensure alignment with the project's goals. 

There are two key aspects of design: 

• High-level design (HLD) provides a broader view, focusing on the system architecture
• Low-level design (LLD) dives deeper into specific components and their interactions.

Key Deliverables:

• System architecture and design specifications
• Prototypes and wireframes
• Database design and interface specifications

Also Read: Software Design Patterns: A Complete Guide for 2025

Phase 3: Development

During the development phase, the software is coded using the design documentation from the previous stage. Developers work within version-controlled environments like Git to manage changes and prevent conflicts. CI/CD pipelines using tools like Jenkins, GitHub Actions, or GitLab CI automate builds, run tests, and deploy artifacts to staging environments for validation.

The core functionality is developed here, with teams applying best practices like modular coding, unit testing, and code linting to ensure maintainability. Regular code reviews, enforced through tools like Bitbucket or Gerrit, promote consistency and catch regressions early.

Key Deliverables:

• Fully functional, tested code
• CI-triggered updates and patches
• Versioned source code and inline documentation

Also Read: GitHub vs GitLab: Key Differences & Features Compared

Phase 4: Testing

Testing is an important step in the SDLC since it ensures that the software works as anticipated and satisfies the requirements. The testing step entails performing a variety of tests, including unit, integration, and system testing, to detect defects and issues. 

Test cases are created based on the project's requirements, and the application is tested in a variety of scenarios to replicate practical use.

This step aids in the early detection of faults, ensuring that any flaws are resolved before deployment. Automated testing tools like JUnit, TestNG, and Selenium help validate functionality at scale, reducing manual effort and turnaround time. In Agile and DevOps workflows, continuous testing is integrated into CI/CD pipelines to maintain codebase quality and detect regressions immediately after each commit.

Key Deliverables:

• Test cases and testing documentation
• Bug reports and issue tracking
• Test environment setup

Phase 5: Deployment

The deployment phase involves releasing the software to users. After passing testing, the software is ready for deployment to a production environment. This phase might include a variety of processes, such as setting up servers, databases, and configuring applications for end users. In other circumstances, the deployment is staged, with a restricted rollout to assess reliability before full-scale adoption.

For Agile or continuous delivery models, deployment might be done in minor, frequent releases, enabling faster feedback and quicker updates. This phase also includes post-deployment activities like APM monitoring (e.g., New Relic, Datadog), log analysis, and integrating user feedback loops to resolve issues.

Key Deliverables:

• Deployed software in a production environment
• User guides and training materials
• Performance and monitoring reports

Also Read: What is the Future of Software Engineering in 2025 & Beyond? Key Trends Shaping the Tech Landscape

Phase 6: Maintenance

After deployment, the software enters the maintenance phase, where it is regularly updated to fix bugs, enhance performance, and ensure security. Software maintenance involves managing patches, updates, and enhancements as the software evolves. 

The maintenance phase is vital for addressing issues that weren't identified during testing or have emerged due to changes in the operating environment.

This is frequently the longest phase, which includes continual support and upgrades to guarantee the software's reliability and effectiveness throughout time. Maintenance also includes monitoring the system, identifying areas for improvement, and putting such changes into action.

Key Deliverables:

• Regular software updates and patches
• Performance improvements
• User support and issue resolution

Phase 7: Retirement

The final phase of the SDLC is the retirement phase, where the software is no longer actively used or supported. This happens when the software ends its lifecycle, and a new version or product replaces it. 

All data is transferred in this phase, and legacy systems are either retired or integrated into new solutions. Proper planning for this phase ensures that no critical data is lost and users are smoothly transitioned to the new system.

Key Deliverables:

• Data migration plan
• System decommissioning
• Final reports and documentation

Check out the table below offering a quick, clear overview of the 7 Key Phases of SDLC:

Also Read: Continuous Delivery vs. Continuous Deployment: Difference Between

Here’s a simple example scenario that covers developing a mobile app for an E-commerce store, illustrating the SDLC process:

Also Read: Data Preprocessing in Machine Learning: 7 Key Steps to Follow, Strategies, & Applications

Now, we will examine the most commonly used SDLC models.

7 Most Common SDLC Models Explained: Key Features

Different projects require different approaches to the software development process. That's where the SDLC models come into play. Each model defines how the software development phases are executed and the order in which they occur. 

Below are the 7 most common SDLC models, with their key features and characteristics.

1. Waterfall Model

The Waterfall model uses a linear, phase-by-phase workflow where each stage, requirements, design, development, testing, and deployment, must be completed before the next begins. It's ideal for environments where project scope, timelines, and deliverables are fixed early, and changes are costly or discouraged. Unlike Agile teams using Jira, Kanban boards, or sprint planning, Waterfall relies on Gantt charts, milestone tracking, and detailed upfront documentation.

Key Features:

• Sequential execution with no overlap between stages
• Uses SRS (Software Requirements Specification), HLD (High-Level Design), and LLD (Low-Level Design) to formalize deliverables
• No sprint-based feedback; testing begins only after full development
• Poor fit for projects requiring flexibility or rapid iterations

Best For: 

Long-term infrastructure builds, ERP systems, or government projects where compliance standards (e.g., ISO 12207, CMMI) and fixed timelines are non-negotiable.

2. V-Shaped Model

The V-shaped model enhances the traditional Waterfall approach by tightly coupling each development phase with a corresponding testing phase, enabling validation and structured quality control. It emphasizes verification and validation (V&V), with test planning beginning as early as the requirements stage. 

Commonly used in regulated industries such as healthcare and aerospace, the model supports traceability matrices, formal reviews, and tools like JUnit, Selenium, and static code analyzers. Its sequential yet test-driven structure ensures defects are identified before integration, making it ideal for projects with strict compliance needs and minimal tolerance for errors.

Key Features:

• Strong focus on early test planning (e.g., writing test cases during design)
• Testing phases mirror development phases in reverse sequence
• Clearly defined checkpoints for audits and quality gates
• Ideal for projects that can’t afford late-stage failures or regression risks

Best For: 

Safety-critical systems, medical devices, or regulated industries requiring formal validation procedures and static analysis tools from the beginning.

3. Agile Model

The Agile model promotes incremental delivery, adaptability, and continuous stakeholder involvement. Projects are divided into sprints (typically 1–3 weeks), during which teams frequently ship working features using tools such as Jira, Scrum methodologies, and CI/CD pipelines. Agile encourages user story mapping, rapid iterations, and active collaboration across cross-functional teams.

Key Features:

• Iterative, feedback-driven development
• Sprints are managed using Scrum or Kanban frameworks
• Integrates well with tools like Git, Jenkins, and Slack
• Continuous integration and delivery ensure faster go-to-market

Best For: 

SaaS platforms, mobile apps, or client-facing systems with frequently evolving requirements and a need for quick feedback loops.

Also Read: Kanban Vs Scrum: Difference Between Kanban and Scrum

4. Lean Model

The Lean SDLC model, derived from lean manufacturing, aims to maximize value with minimal waste. It eliminates non-essential steps, prioritizes customer value, and applies just-in-time development and value stream mapping to reduce inefficiencies. Lean teams use Kanban boards, WIP limits, and tools like Trello or ClickUp to visualize and manage flow.

Key Features:

• Focus on delivering only high-value features
• Eliminates redundant meetings, features, or rework
• Uses pull-based workflows to control resource allocation
• Emphasizes team autonomy, retrospectives, and continuous learning

Best For: 

MVPs, startups, and projects where rapid iteration, resource constraints, or a clear feedback loop are critical.

5. Iterative Model

The Iterative model breaks development into repeated cycles, delivering a functional prototype after each iteration. It enables early testing and refinement, with each iteration building upon the last. Teams often use Jira, TestRail, and Postman to gather feedback and test features throughout the software development process.

Key Features:

• Frequent incremental builds with partial feature sets
• Feedback is gathered after each iteration for refinement
• Early identification of usability or performance gaps
• Supports modular design and parallel development streams

Best For: 

Projects with unclear initial requirements or where gradual evolution is preferred over full upfront planning.

Also Read: What is Agile Project Management? Everything You Need to Know

6. Spiral Model

The Spiral model integrates iterative prototyping with formal risk management. Each cycle, or “spiral,” involves planning, risk analysis, engineering, and evaluation. It supports progressive development while reducing high-risk surprises, making it ideal for complex architectures and compliance-heavy builds.

Key Features:

• Combines Waterfall discipline with Agile flexibility
• Risk-driven with each phase assessing technical, cost, and security risks
• Incorporates stakeholder reviews and formal risk documentation
• Tools like RiskWatch, Enterprise Architect, and GanttPro are used for tracking

Best For: 

Large-scale enterprise systems, government contracts, or regulated industries like healthcare or defense.

7. Big Bang Model

The Big Bang model skips structured planning in favor of rapid coding without a defined scope. Developers build features as they go, often without full specifications. There is minimal documentation, and testing occurs only after the software is complete, which can lead to potential scalability or integration issues.

Key Features:

• No structured requirements or architecture planning
• Development relies on intuition and real-time decisions
• Suitable for throwaway prototypes or proof-of-concepts
• Often uses lightweight tools like VS Code, Notion, and local git repos

Best For: 

Small-scale projects, academic prototypes, or experimental builds where speed and creativity are valued over stability.

Also Read: 48 Software Engineering Projects in 2025 With Source Code

The next section is about integrating security measures into each phase of the SDLC.

How Do You Integrate Security into SDLC?

Ensuring security in the software development life cycle phases protects software against vulnerabilities throughout the development process. Instead of being treated as an afterthought, security is integrated into every process. This proactive strategy reduces risks, improves software quality, and assures industry compliance.

Here's how you can integrate security at each of the software development life cycle phases:

1. Planning and Analysis:

Security begins at the planning stage. At this early stage, the project team must understand the software's security requirements, identify security objectives, and match them with business goals. The team must consider potential threats, compliance requirements, and privacy concerns. Risk assessments should be conducted to anticipate possible vulnerabilities, and security requirements should be specified in the project scope.

• Key Actions:
  • Perform a risk assessment to identify possible threats.
  • Define security requirements (e.g., data encryption, access controls).
  • Ensure compliance with legal and regulatory standards (e.g., GDPR, HIPAA).

2. Design:

During the design phase, security architecture plays a central role. The system must be designed with security by design, incorporating practices like secure coding and secure architecture design. Threat modeling is essential to identify potential attack vectors early. Security measures such as input validation, data encryption, and role-based access control must be included in the design.

• Key Actions:
  • Conduct threat modeling to foresee possible attack methods.
  • Apply secure design principles (e.g., defense in depth).
  • Design for compliance (e.g., secure APIs, data privacy).

3. Development:

In the development phase, secure coding practices are critical. Developers should be trained on secure coding standards (e.g., OWASP Top 10) to prevent vulnerabilities like SQL injection and cross-site scripting (XSS). Automated security tools, such as static code analysis and dependency scanners, should be used to detect vulnerabilities as the code is written.

• Key Actions:
  • Implement secure coding practices.
  • Use automated security tools to detect vulnerabilities in real time.
  • Perform peer code reviews with a focus on security.

4. Testing:

Security testing is an ongoing process, not a final check. It should be integrated throughout the testing phase, where penetration testing, vulnerability scanning, and fuzz testing are essential to find weaknesses in the system. 

Developers should continuously test for security flaws, addressing all vulnerabilities before moving forward. Regular security regression testing ensures that new changes do not introduce new vulnerabilities.

• Key Actions:
  • Perform penetration testing and vulnerability assessments.
  • Use dynamic analysis tools to identify runtime security issues.
  • Conduct security regression testing to maintain a secure codebase.

Also Read: Loose vs Tight Coupling in Java: Key Differences & Examples

5. Deployment:

The deployment phase should be treated with the same level of security as any other phase. This involves ensuring the deployment pipeline is secure, data is encrypted in transit, and access control is enforced. 

Tools like Infrastructure as Code (IaC) can automate secure deployment processes, ensuring the software is deployed in a safe environment. Continuous security monitoring tools must be implemented to track potential threats post-deployment.

• Key Actions:
  • Ensure encrypted communication during deployment.
  • Apply role-based access control for deployment environments.
  • Implement continuous security monitoring for active threat detection.

6. Maintenance:

The maintenance phase is critical for ensuring long-term security. Security patches and software updates must be regularly applied to address new vulnerabilities. Security audits should be scheduled periodically to evaluate the software's defense mechanisms and ensure no new security issues have arisen. Maintenance is also an opportunity to enhance the security posture based on evolving threats.

• Key Actions:
  • Regularly apply security patches to software and dependencies.
  • Conduct security audits to identify new risks.
  • Use automated tools to find and remediate vulnerabilities.

7. Retirement:

In the retirement phase, security should not be overlooked. All data must be securely archived or deleted when the software is decommissioned or replaced. Securely dispose of old systems, and ensure that backups and legacy systems are protected from unauthorized access. Proper data migration or data destruction practices must be followed to avoid data breaches.

• Key Actions:
  • Securely migrate or delete data as needed.
  • Decommission systems with security best practices.
  • Ensure secure disposal of backups and archives.

Let’s now compare SDLC with other popular project management methodologies.

SDLC vs. Other Project Management Methodologies: A Comparative Analysis

The Software Development Life Cycle (SDLC) is extensively used to manage the software development process. However, various techniques and lifecycles offer distinct approaches. Each has particular characteristics, use cases, and benefits. 

Below, we'll compare software development life cycle phases with different project management and development methodologies, highlighting their differences and when to choose one.

1. SDLC vs. ALM (Application Lifecycle Management)

SDLC focuses on the software development process, covering stages from planning to maintenance. It's a structured approach with clear phases designed to deliver software projects efficiently. On the other hand, application lifespan Management (ALM) covers an application's whole lifespan, from conception and development to deployment and maintenance, and finally retirement.

• Key Difference: While SDLC is concerned primarily with the software's development, ALM manages the entire application lifecycle, including project management, testing, integration, deployment, and support.
• When to Choose SDLC: When you need a straightforward, focused approach to software creation, such as for a specific application.
• When to Choose ALM: When looking for a broader methodology that integrates software development with ongoing management, version control, and alignment with business goals.

Use Case:

SDLC suits standalone software projects, such as a Java-based HR portal or a C#-built desktop billing app, focusing strictly on the development phases. ALM, however, spans planning to retirement, integrating tools like Jira, GitHub, and Azure DevOps for version control, sprint management. Utilize ALM when managing large-scale applications that undergo frequent updates, employ branching strategies, and require stakeholder alignment.

2. SDLC vs. Systems Development Lifecycle (SDLC)

At first glance, the term Systems Development Lifecycle (SDLC) may seem similar to Software Development Life Cycle (SDLC), but the key difference lies in their scope. Systems Development Lifecycle focuses on the broader spectrum of creating a system, including hardware, software, and user interactions. Software development life cycle phases are specifically focused on the software aspects alone.

• Key Difference: While SDLC (Software) refers exclusively to software, SDLC (Systems) includes broader system considerations such as hardware, networking, and integration with other systems.
• When to Choose SDLC (Software): Projects focused strictly on software development and applications.
• When to Choose SDLC (Systems): For projects involving a comprehensive system development, including software, hardware, and other infrastructural elements.

Use Case:

Software SDLC fits projects like building a Python Flask web service using HTTP APIs, HTML, CSS, and JavaScript for frontend interactions. Systems SDLC is more suited for solutions like an ATM network or IoT deployment where software must interface with embedded systems, hardware sensors, and network switches. Choose Systems SDLC when both software and physical components need synchronized development and deployment.

Also Read: The Ultimate Guide to Python Web Development: Fundamental Concepts Explained

3. SDLC vs. STLC (Software Testing Lifecycle)

The Software Testing Life Cycle (STLC) focuses specifically on the testing aspect of software development. It includes planning, designing, executing, and validating tests to ensure the software works as expected. While SDLC covers the full software development process from planning to maintenance, STLC focuses on the testing and quality assurance phases.

• Key Difference: SDLC is a complete cycle, whereas STLC is a subset of SDLC focused exclusively on ensuring the quality and functionality of the software.
• When to Choose SDLC: When you need a comprehensive, end-to-end approach to software creation.
• When to Choose STLC: If you're looking to focus purely on the software's testing and quality assurance aspect after its development phase.

Use Case:

SDLC governs end-to-end product development, including building a TypeScript-based Angular app with a Node.js backend and deploying it on AWS using CI/CD. STLC handles only the quality assurance layer, utilizing frameworks such as JUnit, Selenium, and PyTest to test HTTP responses, UI flows, and data validations. Use STLC when QA teams need to operate independently with test automation pipelines embedded into the broader SDLC.

4. SDLC vs. DevOps

DevOps and SDLC are closely related but differ in their approach and scope. DevOps integrates development and operations teams, emphasizing continuous collaboration, automation, and integration to speed up software delivery and deployment. On the other hand, SDLC follows a structured, phased approach to building software, with testing and deployment often separate, sequential stages.

• Key Difference: SDLC is sequential and focuses on the phases from design to maintenance. At the same time, DevOps promotes a continuous, iterative cycle that fosters collaboration between development and operations throughout the entire process.
• When to Choose SDLC: When you need clear, distinct phases for software development, particularly in more traditional, waterfall-like processes.
• When to Choose DevOps: If your firm focuses on continuous integration and delivery (CI/CD), automating procedures, and promoting real-time team collaboration.

Use Case:

Utilize SDLC for linear development flows, such as a Java ERP module that follows a fixed release plan and is deployed manually on-premises. DevOps enables automated deployments for containerized Python APIs or C# microservices, utilizing tools such as Docker, and real-time monitoring via Prometheus or Grafana. Adopt DevOps when working with CI/CD workflows and cross-functional teams managing frequent code integrations and environment provisioning.

Also Read: Combining Machine Learning and Data Visualization for Accurate Data Predictions

5. SDLC vs. PDLC (Product Development Lifecycle)

While SDLC specifically focuses on the development of software, PDLC (Product Development Lifecycle) extends beyond software to include product concept, market analysis, prototyping, and post-launch customer support. PDLC covers the entire lifecycle of a product from ideation to the end of its useful life, incorporating strategic decisions about product design, user experience, and feature sets.

• Key Difference: SDLC is software-specific, whereas PDLC encompasses everything from the initial product idea through design, development, and market introduction.
• When to Choose SDLC: When you need to develop and manage software in a structured way, focusing on the product's technical aspects.
• When to Choose PDLC: If you're developing a product with a broader scope, including market research, prototyping, and design beyond just the software.

Use Case:

SDLC is ideal for engineering teams building a backend authentication API in Python or Java, where product-market alignment is already defined. PDLC, on the other hand, covers everything from user story mapping and prototyping in Figma to testing a React frontend powered by TypeScript and deploying to web and mobile platforms. Opt for PDLC when product development involves business decisions, user analytics, and multi-platform synchronization beyond code.

6. SDLC vs. SRLC (Software Release Life Cycle)

Software Release Life Cycle (SRLC) focuses on software's release and deployment aspects. This includes version control, deployment strategies, and updates. It starts when a software version is deemed ready for production and ends when it is fully deployed and live for users. SDLC, however, focuses on all stages from planning to maintenance, with deployment as just one part of the cycle.

• Key Difference: SDLC is broader, covering the entire software development process, while SRLC focuses specifically on the release and deployment of software.
• When to Choose SDLC: For end-to-end development management of software projects.
• When to Choose SRLC: When you are focused on the release process, ensuring smooth deployment and post-launch updates.

Use Case:

In SDLC, a team might build a complete e-learning system using Django, HTML/CSS, and JavaScript, handling feature delivery, QA, and maintenance phases. SRLC kicks in once the application reaches production, managing deployment rollouts, hotfixes, semantic versioning, and change logs using tools such as Nginx and AWS CodeDeploy. Use SRLC when you're focused on safely releasing features, version control, and ensuring rollback-ready environments for live systems.

But, why SDLC? The next section will cover the core benefits.

Key Benefits of Adopting the Software Development Life Cycle Phases

Optimizing the software development life cycle phases integrates CI/CD, automated testing, and code quality tools into the pipeline, enabling precise defect detection, optimized resource management, and real-time performance monitoring. It enhances agile workflows, version control, and risk mitigation through continuous security scanning and regression testing.

Let’s explore these benefits:

1. Improved Quality

SDLC improves software quality through continuous integration (CI) and continuous testing (CT), which allow for automated validation and early identification of defects. By integrating tools like static code analysis and unit testing within each phase, developers can proactively resolve issues at the code level, preventing defects from propagating to later stages. 

Automated security scans and performance benchmarks throughout the development phases ensure that the software adheres to functional, security, and performance standards, leading to more reliable and scalable applications.

• Continuous integration and testing with tools like JUnit, Selenium, and PyTest enable up to 90% code coverage, helping teams catch regressions early and reduce Mean Time To Resolution (MTTR) by over 40% in microservice-based architectures.
• Integrated static code analysis using SonarQube and security scanners, such as Bandit, identifies issues like unsafe imports, code duplication, and injection risks before merge, thereby enforcing compliance with coding and security standards.
• Performance and scalability testing with JMeter or Locust enables teams to identify endpoints that exceed latency thresholds (e.g., 500ms), ensuring releases meet SLA targets and remain stable under production-level traffic.

Also Read: Software Developer vs Software Engineer: Key Differences, Similarities and More

2. Reduced Costs

SDLC promotes cost savings by facilitating early-stage bug detection and optimizing resource allocation. Defects are identified early by incorporating automated testing frameworks and code reviews into the CI/CD pipeline, minimizing the need for expensive post-release fixes. 

Moreover, using tools like project management software integrated with version control systems (e.g., Git) ensures precise tracking of resources, preventing scope creep. Agile methodologies within SDLC, such as Scrum or Kanban, support iterative development, improve predictability, and help development teams stay within budget.

• Early-stage defect detection using automated testing frameworks (e.g., JUnit, PyTest) and peer code reviews within CI/CD pipelines reduces the likelihood of post-release failures, cutting remediation costs by up to 80%.
• Integration of project management tools like Jira or ClickUp with Git-based version control ensures accurate task tracking, enabling better forecasting, workload distribution, and mitigation of scope creep.
• Agile practices such as Scrum and Kanban, implemented within the SDLC, promote sprint-based resource planning and continuous reprioritization, helping teams stay on schedule and within allocated budgets.

3. Better Project Control

A well-implemented SDLC enhances project control by integrating DevOps practices and applying Agile methodologies for adaptive planning. 

By incorporating automated deployment pipelines and configuration management tools (e.g., Jenkins, Docker, Kubernetes), teams can continuously monitor progress through version control systems and automatically deploy updated software builds. This ensures transparency at every project phase, enabling project managers to address potential delays and allocate resources dynamically and proactively. 

Furthermore, data analytics and project tracking tools like JIRA and Trello offer live visibility into development timelines, empowering teams to make informed decisions.

• DevOps integration through tools like Jenkins, Docker, and Kubernetes enables automated deployments, configuration consistency, and real-time build tracking via Git-based version control systems.
• Agile methodologies such as Scrum and Kanban support adaptive planning and sprint retrospectives, allowing teams to adjust workloads dynamically and prevent delivery bottlenecks.
• Platforms like Jira, Trello, and Azure Boards provide real-time dashboards and velocity metrics, offering complete visibility into progress, blockers, and resource allocation at every phase.

Also Read: What Is Task Management? 14 Key Tools and Strategies for Success in 2025

4. Risk Management

SDLC strengthens risk management through early risk identification and direct mitigation techniques. In planning, teams apply risk-based testing and threat modeling tools, such as OWASP's Threat Dragon, to identify potential security vulnerabilities. 

Also, integrating tools for dynamic application security testing (DAST) and static application security testing (SAST) helps uncover threats during development, reducing the risk of late-stage vulnerabilities. Regular regression, load testing, and advanced monitoring solutions allow teams to promptly address performance bottlenecks and security risks, ensuring stable and secure software delivery.

• Early-stage threat modeling using tools like OWASP Threat Dragon and risk-based testing strategies during planning help identify potential vulnerabilities before coding begins.
• Integrating SAST (e.g., SonarQube) and DAST tools (e.g., OWASP ZAP, Burp Suite) into CI/CD pipelines uncovers security flaws and logic errors during development, reducing production-stage exposure.
• Ongoing regression testing, load testing with JMeter, and runtime monitoring using tools like New Relic or AppDynamics ensure system resilience under stress and allow rapid mitigation of risks in real-time.

Also Read: Top 5 Productivity Tools for Software Engineers

While SDLC offers many benefits, effective risk management is vital to project success.

upGrad’s Exclusive Software Development Webinar for you –

SAAS Business – What is So Different?

SDLC Risk Management: Best Practices & Common Errors

Effective risk management is key to delivering successful projects. However, many software development life cycle phases encounter risks that can derail progress if not properly managed. 

Below are some of the most common risks encountered during the SDLC process and the best practices to mitigate them.

1. Scope Creep

Scope creep occurs when project requirements continuously evolve without proper documentation or control. This leads to adding new features or tasks that were not initially planned for, causing delays, confusion, and budget overruns.

Solution:

• Clearly define project requirements and ensure they are well-documented before starting the development process.
• Implement a change control process where any changes to the scope are carefully reviewed, evaluated, and approved based on their impact on timelines and resources.
• Keep stakeholders aligned and the scope under control by using project management tools.

Example Scenario:

A team building a Java-based inventory management system receives repeated requests to add reporting features mid-sprint. By routing each request through Jira’s change request workflow, assessing resource impact, and adjusting the sprint backlog accordingly, the team prevents disruption while maintaining delivery velocity.

Also Read: Importance of Product Management in Software Industry: 11 Essential Insights for 2025

2. Budget Overruns

Budget overruns happen when a project exceeds its allocated budget due to underestimation of resources, unexpected complexities, or uncontrolled scope changes.

Solution:

• Accurate resource estimation: At the planning stage, make realistic predictions about costs, including hardware, software, and human resources.
• Set up a contingency budget to cover unexpected expenditures, but only use it for justified risks.
• Regularly track spending and compare it against the budget to identify any deviations early and take corrective action.

Example Scenario:

A team developing a Python-based analytics dashboard underestimated costs for third-party API integrations. By using Jira with cost tracking plugins, they identified budget slippage by sprint 3, paused non-critical enhancements, and reallocated their buffer to critical data processing modules.

3. Lack of Stakeholder Alignment

Expectations can diverge when there's poor communication or a lack of alignment between stakeholders, leading to dissatisfaction and project delays. Misaligned goals can result in building software that doesn't meet business objectives or user needs.

Solution:

• Hold regular meetings with all key stakeholders to discuss progress, address concerns, and ensure alignment.
• Use collaborative tools to keep all parties updated and involved in decision-making.
• Make a project charter to outline clear goals and roadmaps to keep everyone on the same page.

Example Scenario:

During development of a JavaScript-based CRM interface, sales and product teams had differing expectations for report filters. By documenting agreed-upon requirements in Confluence and linking them to epics in Jira, the development team avoided miscommunication, built the correct UI logic, and reduced iteration cycles.

4. Poor Requirement Gathering

Inadequate or unclear requirement gathering often leads to misunderstood needs, incomplete functionalities, or software that does not meet the original objectives. Missing or ambiguous requirements can cause rework and delays.

Solution:

• Involve all essential stakeholders in the requirements collecting process to ensure all viewpoints are considered.
• Use user stories, prototypes, or mockups to visualize requirements and avoid ambiguity.
• Regularly validate requirements to ensure they remain accurate and aligned with business goals.

Example Scenario:

A team building a TypeScript-based admin portal faced ambiguity over permission roles. By creating Figma mockups and linking approval flows to Jira stories, they aligned expectations early and avoided downstream redesign.

5. Inadequate Testing and Quality Assurance

Skipping or rushing through the testing phase can result in undetected bugs or software that fails to meet user expectations, leading to post-release issues, poor customer satisfaction, and additional costs for fixes.

Solution:

• Integrate continuous testing into the SDLC process, ensuring tests are performed at each stage of development.
• Automate testing where possible to detect issues quickly and ensure comprehensive coverage.
• Involve testers early in the software development process and use feedback loops to address problems promptly.

Example Scenario:
A Python Flask API deployed without proper regression testing caused invoice calculation errors. After automating tests via PyTest and integrating them into the GitHub Actions CI pipeline, the team achieved stable releases in subsequent sprints.

Also Read: Top 10 Front-End Developer Project Ideas & Topics For Beginners [2025]

6. Underestimating the Complexity of Integration

Integrating new software with existing systems might be more difficult than expected. Poor integration planning can cause system breakdowns, performance concerns, and higher-than-expected expenditures.

Solution:

• Develop a clear integration plan that includes time for testing and adjustments.
• Perform early integration testing to identify and resolve issues before they escalate.
• Use modular architecture where possible, which allows for easier integration and reduces the impact of any integration failures.

Example Scenario:
A Java microservice failed to sync with a legacy billing platform due to XML format discrepancies. By introducing API contracts and running nightly test suites via Jenkins, the team resolved integration friction within the CI cycle.

7. Security Vulnerabilities

Neglecting security during development can lead to vulnerabilities that expose the software to attacks, such as data breaches, SQL injection, or cross-site scripting (XSS).

Solution:

• Adopt secure coding methods and include security throughout all stages of the SDLC, including design and development.
• Integrate automated security testing tools into the CI/CD pipeline to detect vulnerabilities early.
• Conduct frequent security assessments and software updates to combat evolving threats. 

Example Scenario:

A C# ASP.NET web app was flagged for input sanitization gaps during a security audit. By integrating Snyk and adding static checks via SonarQube, the team prevented similar risks in future releases.

Now, let’s explore how to choose the right SDLC model for your specific project needs.

How to Choose the Right SDLC Model for Your Project?

Choosing the right SDLC model can determine the success of your project. While different models serve different needs, the key is understanding your project's specific requirements and selecting the model that aligns with them.

Here's a simplified guide to help you choose the best SDLC model based on common project needs:

1. When Requirements Are Clear and Unchanging

Best Model: Waterfall

• Why? Waterfall model’s linear, structured approach works best when the scope and timeline are fixed, such as for regulatory or compliance-driven projects.
• Example: Building a payroll system for a corporation where the functionality and requirements are well-established.

2. When You Need Flexibility and Frequent Changes

Best Model: Agile

• Why? The Agile model is perfect for projects where requirements are expected to evolve. It works well for projects where frequent feedback from stakeholders is essential, allowing the development team to adapt to changing needs quickly.
• Example: Developing a mobile app for a startup, where user feedback drives regular feature updates.

3. When You Need Early Risk Assessment and Complex Project Scope

Best Model: Spiral

• Why? The Spiral model is designed for large, complex projects where risk management is a priority. It allows for iterative development and emphasizes risk analysis at every stage, which is crucial for projects with high uncertainty or critical security needs.
• Example: Developing a defense or healthcare application that requires continuous risk assessment and updates based on regulatory changes.

Also Read: What is Software Architecture? Tools, Design & Examples

4. When You Need Fast Development with Continuous Delivery

Best Model: DevOps (a modern variation of Agile)

• Why? DevOps blends development and operations, focusing on continuous integration and delivery. It is perfect for applications that demand quick deployment and continual upgrades since it provides continuous testing, integration, and deployment.
• Example: E-commerce platforms that need frequent updates, new features, and rapid delivery to stay competitive.

5. When You Want Detailed, Sequential Development with Minimal Testing Requirements

Best Model: V-Shaped

• Why? The V-shaped model, like the Waterfall model, places a strong emphasis on validation and verification at every turn. It's ideal when you need a clear sequence of steps but still want to incorporate early testing at each phase.
• Example: Developing embedded systems where precise validation is needed at each development stage.

6. When Your Project Has a Well-Defined Scope and Tight Deadlines

Best Model: Lean

• Why? The Lean model focuses on maximizing value while minimizing waste. It is ideal for projects where resources are limited and efficiency is a priority, ensuring the software is delivered with minimal time and effort spent on non-essential tasks.
• Example: Launching an MVP (minimum viable product) for a startup with limited resources.

Also Read: Top 8 Process Models in Software Engineering

7. When You Need Incremental Development with Regular Testing

Best Model: Iterative

• Why? The Iterative model allows you to develop software incrementally, improving each version with user or stakeholder feedback. This model is well-suited for projects that must be built in stages, with regular testing to ensure quality at each step.
• Example: Create a large-scale corporate software solution that will improve over time, depending on user feedback. 

Conclusion

Understanding the seven phases of the Software Development Life Cycle (SDLC) helps you build efficient, scalable, and error-resistant software. To apply SDLC effectively, ensure each phase is clearly documented, involve stakeholders early, and choose the right model—Agile for iterative delivery, Waterfall for fixed scope, or Spiral for high-risk projects. Align each step with your project’s goals to improve quality and reduce rework.

However, many professionals struggle to connect SDLC theory with real-world execution. That’s where upGrad helps. As a trusted learning partner, upGrad offers programs that turn SDLC knowledge into practical skills.

If you're feeling uncertain about the best path forward, you're not alone. Let upGrad’s expert counselors help you identify the right course to bridge your skill gaps and fast-track your journey in full-stack development. Get in touch today or visit our offline centers and start your path toward success!

Boost your career with our popular Software Engineering courses, offering hands-on training and expert guidance to turn you into a skilled software developer.

Explore our Popular Software Engineering Courses

Master in-demand Software Development skills like coding, system design, DevOps, and agile methodologies to excel in today’s competitive tech industry.

In-Demand Software Development Skills

Stay informed with our widely-read Software Development articles, covering everything from coding techniques to the latest advancements in software engineering.

Read our Popular Articles related to Software

Reference: 
https://www.fortunebusinessinsights.com/generative-ai-in-software-development-lifecycle-market-109041