What is software testing?

Software testing is the practice of verifying that software meets defined user needs and business requirements. It checks behaviour against acceptance criteria and business rules, protects data and privacy, upholds security and accessibility standards, and confirms reliability across supported devices, browsers and integrations. It reduces risk, improves reliability and performance, and protects customer experience before changes reach production.

Teams use different levels of testing, a mix of functional and non-functional checks, and the right balance of manual and automated approaches to achieve quality at speed.

In this guide

• Why software testing matters
• Levels of software testing
• What are the Software Development Life Cycle (SDLC) and the Software Testing Life Cycle (STLC) in testing?
• Functional vs non-functional testing
• Manual vs automated testing
• How is software testing conducted
• Best practices that reduce risk and cost
• Testing methodologies and techniques
• The future of software testing
• FAQs
• What next

Why software testing matters

Effective testing prevents costly defects, shortens feedback loops, and increases confidence to release more often. It helps teams:

• Validate that features behave as intended for real users.
• Control the cost of quality by finding issues earlier, not in production.
• Demonstrate non-functional qualities such as performance, security and accessibility.
• Build measurable confidence for go-live.

For example, contract tests and targeted regression can catch a payment gateway change that would otherwise block certain cards at checkout. Load and spike testing reveal slowdowns before a campaign goes live, so teams fix bottlenecks and protect conversion. Boundary and decision-table tests prevent VAT rounding errors on high-value invoices, reducing refunds and support calls. Automated dependency and basic security scans stop vulnerable builds from shipping, and accessibility checks with assistive technology ensure forms can be completed by keyboard and screen reader users.

Levels of software testing

Testing is organised into levels that build confidence from small units of code to full business workflows. Each level has a clear purpose, typical owners, and the kinds of defects it is best at catching.

Unit testing

Unit tests exercise the smallest testable parts of your code in isolation, such as a function or class method. They run quickly, give precise failure signals, and are ideal for catching logic errors early. Good unit tests avoid external dependencies by using fakes or mocks, and sit in CI so regressions are caught on every commit.

Component testing

Component tests verify a cohesive piece of the application made up of multiple units, such as a service, UI widget, or repository layer. They validate public interfaces and behaviour at the boundaries, often with lightweight stubs for upstream or downstream dependencies. This level reveals defects in configuration, state handling, and interactions within the component.

Integration testing

Integration tests check that modules, services, and data stores work together as intended. They focus on API contracts, message formats, authentication, and real data flows between systems. Use a mix of contract tests for speed and reliability, plus targeted end-to-end paths for critical journeys. Aim for stable test data and deterministic environments to reduce flakiness. Insert grey block: New to this level? Read our plain-English guide to integration testing for examples and techniques. Read our guide to integration testing for examples and techniques.

System testing

System tests validate the complete application in an environment that mirrors production. They cover full user journeys across interfaces and services, including browsers, devices, and third-party integrations. Use automation for repeatable smoke and regression suites, and include non-functional checks such as performance, accessibility, security, and reliability where appropriate.

Acceptance testing (UAT)

Acceptance tests confirm the solution meets business goals and user expectations. They are typically designed with product owners and end users, mapped to explicit acceptance criteria, and run with realistic data and scenarios. Clear pass or fail outcomes support go-live decisions, with any gaps fed back into the backlog for remediation.

What are the SDLC and STLC in testing?

The software development life cycle (SDLC) is the full process to plan, build, deploy and maintain software. The software testing life cycle (STLC) sits inside the SDLC and focuses on quality activities for each phase: planning tests, designing cases and data, setting up environments, executing tests, reporting results and improving the process.

What are the seven steps of the software development lifecycle (SDLC)?

1. Planning: Define goals, scope, stakeholders, success metrics, timeline and budget. Identify high-level risks and outline the delivery approach.
2. Requirements: Capture functional and non-functional needs with clear acceptance criteria. Prioritise the backlog and establish traceability.
3. Design: Choose architecture, data models, interfaces and security patterns. Produce UX flows and plan for performance, reliability and testability.
4. Development: Build features to standards with version control and CI. Write unit/component tests and keep documentation current.
5. Testing: Validate against acceptance criteria and quality attributes. Run unit, integration, system and UAT, then log and triage defects.
6. Deployment: Package and release safely using automation, feature flags and rollback. Verify with smoke checks and communicate changes.
7. Maintenance: Monitor, support and optimise in production. Fix defects, update dependencies, deliver enhancements and feed learning into the next cycle.

Five-phase variants group planning and requirements together, and combine deployment and maintenance.

What are the seven steps in the software testing life cycle (STLC)?

1. Requirement Analysis: Clarify scope, risks and acceptance criteria with stakeholders, then map each requirement to tests so nothing is missed.
2. Test Planning: Select test types by risk, define entry and exit criteria, allocate people/tools/environments, and set timelines and reporting.
3. Test Design: Write concise, high-value cases and data using proven techniques; include positive, negative and edge paths with clear expected results.
4. Environment Setup: Provision representative environments and data, align versions and configurations, and prove readiness with smoke checks.
5. Test Execution: Run tests in priority order, capture evidence, and triage failures fast to separate environment issues from true defects.
6. Defect Tracking and Re-testing: Log defects with reproducible detail, fix and verify them, then run targeted regression to prevent recurrence.
7. Test Closure: Confirm exit criteria and sign-off, report coverage and residual risk, archive evidence, and record lessons for the next cycle.

What is the correct order of software testing?

Unit → Integration → System → Acceptance (UAT)

The order moves from small and isolated to broad and real: unit finds cheap, localised defects; integration checks components work together; system validates the whole application in a production-like environment; acceptance confirms business fit with real users. This sequence keeps feedback fast and avoids late-stage surprises.

Map SDLC and STLC to your delivery model with our Software Testing & Quality Assurance Services.

Why following the STLC is crucial

Adhering to a structured STLC framework offers several advantages. It ensures consistency across testing activities, minimises risks by identifying issues early, and provides transparency for all stakeholders. A well-executed STLC aligns testing efforts with project goals, ultimately delivering software that meets user expectations and performs flawlessly in production.

Functional vs non-functional testing

Functional testing proves what the system does. It includes unit and component tests, API testing, UI testing, regression and end-to-end scenarios.

Non-functional testing proves how well the system behaves under realistic conditions. It covers performance and load, security, accessibility, usability and reliability.

Manual vs automated testing

Both approaches are valuable. The question is where each provides the best return. Use this comparison to choose the right fit per scenario.

Decision tips:

• Automate stable, repeatable and high-risk scenarios first to maximise return
• Keep manual for exploration, UX/accessibility and early discovery on new features
• Blend both in CI/CD: fast automated checks on each change, with targeted manual sessions for depth

If you are building a business case, explore the benefits of test automation and where it returns the most value. Our consultants can help you design the right roadmap through Software Testing & Quality Assurance Services and Performance and Load Testing Services.

How is software testing conducted?

A simple view of the process many teams follow:

1. Analyse requirements and risks, define acceptance criteria
2. Plan the approach, environments, data and tools
3. Design test cases and data, prioritised by risk
4. Set up environments and pipelines
5. Execute tests and log defects with clear reproduction steps
6. Re-test fixes and run targeted regression
7. Report outcomes, trends and improvements

Next, learn how to measure quality and deliver it with metrics that guide better decisions.

Best software testing practices that reduce risk and cost

• Shift left: Involve quality early and run fast checks on every change.
• Risk-based focus: Prioritise tests around what matters most to users and the business.
• Representative environments and data: Keep test data realistic and protect privacy.
• Maintain a living regression suite: Review coverage regularly and remove brittle tests.
• Observability and metrics: Track defect escape rate, mean time to recovery and performance budgets to guide decisions.
• Pairing and collaboration: Align developers, testers and product owners on acceptance criteria and behaviour-driven examples.

Testing methodologies and techniques

Understanding the different testing methods and techniques is essential for creating a robust QA process. Each method serves a unique purpose, ensuring that every aspect of your software is evaluated thoroughly.

Black box vs. White box testing

• Black Box Testing: This approach focuses on testing the software’s functionality without any knowledge of its internal code or structure. It validates user-facing features, ensuring they work as expected in real-world scenarios. Black box testing is ideal for functional, usability, and acceptance testing.
• White Box Testing: White box testing dives deeper into the code itself, analysing its structure, logic, and flow. This technique is particularly useful for verifying algorithm accuracy, ensuring optimal performance, and identifying potential security vulnerabilities within the codebase.

Exploratory testing

Exploratory testing involves a hands-on approach where testers interact with the software dynamically, uncovering issues without predefined test cases. This technique is invaluable for identifying edge cases and unexpected behaviours, particularly in complex or rapidly evolving applications.

Regression testing

Regression testing ensures that any new code changes or updates do not disrupt existing features or introduce new defects. By re-executing test cases from earlier stages, this method safeguards against unintended side effects, maintaining the software’s integrity after updates.

Acceptance testing

Acceptance testing serves as the final check to validate that the software meets user requirements and expectations. Conducted by end-users, it confirms that the system aligns with real-world needs and is ready for deployment.

The future of software testing

The future of software testing is about releasing faster with clearer proof of quality. Testing is moving into the build pipeline so teams get targeted feedback while work is still fresh.

AI will help design, update and prioritise tests, reduce repetitive effort, and highlight gaps, while people focus on risk, user experience and compliance.

As systems rely on many small services, teams will check the contracts between those services to catch breaking changes before customers see them. Test environments will be created on demand so checks run in conditions that mirror production, supported by strong observability and security checks as part of the delivery flow. The outcome is simpler: quicker cycles, fewer surprises, and better evidence that performance, security and accessibility are in hand.

Will testers be replaced by AI?

AI accelerates test generation and maintenance, yet it does not replace human judgement in risk analysis, UX and accessibility evaluation, security context and governance. Roles shift toward tooling, data, prompts and oversight.

Frequently Asked Questions (FAQs) about software testing