Foreword

In 2026, AIAgent and cloud-native technologies are reconstructing the entire software development process, and the software testing industry is ushering in a key transformation from "manual back-up" to "intelligent front-end". Data shows that the average monthly failure rate of traditional test scripts is still as high as 25%, and maintenance costs account for more than 60% of the total testing workload. However, AI-driven testing solutions have achieved several times improvement in efficiency and have become the core choice for quality assurance in industries such as finance and automobiles.

 

Facing the new paradigm of "human-machine collaboration", whether you are a novice who wants to get started or a practitioner who is seeking to upgrade his skills, you need a knowledge system that takes into account basic logic and cutting-edge trends. To this end, the TesterHome community has launched a series of articles called "Advanced Testing Quality", which starts from the core understanding of testing, gradually deepens into process specifications, tool operations, and special practices, and finally connects to cutting-edge fields such as AI testing and cloud native testing. Through systematic content output and practical case dismantling, it helps everyone build testing capabilities that adapt to industry changes. This series will continue to be updated, so stay tuned!

 

1. Introduction: Full-process Testing is the "Lifeline" of Quality Assurance

Software quality assurance does not rely on testing in a single link, but needs to cover the entire process from code writing to product delivery - from the smallest code unit, to the interaction between modules, to the operation of the entire system, and finally to the verification of actual user usage scenarios. Each link needs to be escorted by the corresponding test level and test type.

 

With the popularization of technology trends such as agile development, DevOps, and cloud native, the boundaries of test levels are gradually blurred, and test types are constantly expanding (such as new cloud native tests, AI robustness tests, etc.). This article will systematically dismantle the core test levels of "unit testing - integration testing - system testing - acceptance testing", sort out the core points of common test types such as function, performance, and security. At the same time, combined with cutting-edge technology scenarios, it will explain the adaptation strategy of test levels and types to help everyone build a "full-process, multi-dimensional" testing understanding and master the testing focus and practice methods at different stages.

 

2. Core Testing Levels: Four-level Progressive Testing from "Code Unit" to "User Acceptance"

The core logic of the four-level progressive testing level is "from small to large, from inside to outside, from local to whole". By gradually expanding the test scope, defects are filtered layer by layer to ensure the quality of the final delivered product. The goals, objects, and methods of the four core levels are clearly different, and they must follow the principle of "the quality of the upper level depends on the quality of the next level" (for example, system testing must be carried out based on the passing of unit testing and integration testing).

 

(1) Unit Testing: "Minimum Granularity Verification" at the Code Level

1. Core Definition: Unit testing is the testing of the smallest testable unit (such as function, method, class) in the software. The purpose is to verify whether a single unit meets the design requirements and whether it can correctly perform the established function.

2. Core Goal: Discover code-level defects (such as logic errors, syntax errors, boundary value processing exceptions) as early as possible, ensure the independence and correctness of each unit, and reduce the number of defects in subsequent integration stages.

3. Test Object: Single function, method, class (such as methods in Java, functions in Python, front-end component functions).

4. Test Timing and Responsible Person: The development stage (after the code is written) is led by developers (following the principle of "who develops, who tests"), and is the core link of "shift left testing".

5. Commonly Used Methods and Tools

    - Method: White box testing (needs to understand the internal logic of the code), focusing on covering boundary values, abnormal scenarios (such as empty parameters, parameters out of range), and normal business logic.

    - Tools: Backend (Java: JUnit, TestNG; Python: pytest; Go: GoTest); Front-end (JavaScript: Jest, Mocha).

6. Cutting-edge Practice: AI-assisted Unit Testing

    Traditional unit testing requires developers to manually write test cases, which is inefficient. In the smart era, AI tools can automatically generate unit test cases: such as Amazon CodeWhisperer and GitHub Copilot can automatically generate test cases including normal scenarios, boundary values, and abnormal scenarios based on code logic; Some tools (such as Diffblue Cover) can also automatically analyze code coverage and supplement uncovered test scenarios.

    For example, after the back-end team of an Internet company introduced Diffblue Cover, unit test case generation efficiency increased by 60%, and code coverage increased from 65% to 85%.

7.  Case

    For the "order amount calculation function" of an e-commerce platform (input product unit price, quantity, discount, output final amount), developers wrote unit test cases through JUnit, covering "normal discount", "discount is 0", "quantity is 0", "unit price is empty" and other scenarios, and discovered in advance the defect of "amount calculation error when discount is 0" to avoid affecting the entire order process after integration.

 

(2) Integration Testing: "Collaborative Verification" of Module Interaction

1. Core Definition: Integration testing is to combine multiple modules that have passed unit tests to test whether the interface interaction between modules is normal and meets the design requirements.

2. Core Goal: Discover defects in the interface between modules (such as parameter transfer errors, incompatible data formats, abnormal interface call timing), and verify the correctness of the module's collaborative work.

3. Test Object: Interfaces between modules (such as API calls between microservices, front-end and back-end interfaces, and interactive interfaces between databases and applications).

4. Test Timing and Responsible Person: After the unit test is completed, before the system test; It can be led by developers or testers, and most agile teams are completed by "development + testing" collaboration.

5. Commonly Used Methods and Tools

    - Method: Gray box testing (you need to understand the interface logic between modules and do not need to pay attention to the internal implementation of the module), focusing on testing interface parameters, return values, exception handling, and timeout mechanisms.

    - Tools: Interface testing tools (Postman, Postwoman), automation frameworks (RestAssured, SoapUI), Mock tools (Mockito, WireMock, used to simulate the return results of dependent modules).

6. Cutting-edge Practice: Integration Testing in a Cloud-native Environment**

    Under a cloud-native architecture (microservices, containerization), there are a large number of modules and a dynamic deployment environment. Integration testing needs to adapt to the "distributed and dynamic" characteristics:

    ① Use a containerized testing environment (such as Docker+Kind) to quickly build a multi-module integration testing environment to avoid environmental inconsistencies;

    ② Introduce a service grid (such as Istio) and implement isolation testing between modules through traffic hijacking (for example, when testing module A, the interface of module Mock B returns);

    ③ Interface automated testing is integrated with the CI/CD pipeline. Integration testing is automatically triggered after code submission to ensure interface compatibility after module updates.

7. Case

    Integration test of "user module" and "order module" of a certain microservice architecture. Testers wrote automated scripts through RestAssured to test the interface interaction process of "user creates order" (the user module returns the user ID, and the order module receives the ID and creates the order). They found the defect that "when the user ID is a long integer, the order module receives it as a string, resulting in incompatible data formats." The interface data format was optimized in a timely manner.

 

(3) System Testing: "Comprehensive Verification" of Overall Functions and Non-functions

1. Core Definition: System testing takes the entire software system as the test object and verifies whether the overall functions, performance, compatibility, security, etc. of the system meet the requirements specifications in a real or simulated operating environment.

2. Core Goal: Comprehensively verify the "overall usability" of the system, discover system-level defects (such as missing functions, performance bottlenecks, compatibility issues, security vulnerabilities), and ensure that the system meets the core business needs of users.

3. Test Object: The entire software system (including front-end, back-end, database, and third-party dependent services).

4. Test Timing and Responsible Person: After the integration test is completed and before the acceptance test; Led by testers, with product and developer participation when necessary.

5. Commonly Used Methods and Tools

    - Method: Mainly black box testing (no need to understand the internal logic of the system, only focusing on input, output and expected results), combined with gray box testing (such as analyzing system log positioning issues); It needs to cover multiple dimensions such as function, performance, compatibility, and security.

    - Tools: Functional testing (Selenium, Cypress, Appium), performance testing (JMeter, LoadRunner), compatibility testing (BrowserStack, SauceLabs), security testing (OWASP ZAP, Nessus).

6. Cutting-edge Practice: AI-driven Automated System Testing

    Traditional system testing has low manual execution efficiency and high regression testing costs. AI technology can achieve:

    ① Visual AI testing: through image recognition technology, automatically verify whether the display and layout of front-end page elements are correct (such as Applitools Eyes), adapting to multi-device and multi-resolution scenarios;

    ② Intelligent regression testing: AI tools (such as Mabl, Testim) automatically analyze the impact scope of code changes and only execute affected test cases to reduce the workload of regression testing;

    ③ Full-link stress testing: Combined with chaos engineering (such as Chaos Monkey), simulate abnormal scenarios of distributed systems (such as service outages, network delays), and test the fault tolerance and stability of the system.

7. Case

    System testing of a short video APP. The test team used Appium to verify the functional compatibility of different models of iOS and Android (such as video playback, commenting, and sharing functions); Use JMeter to simulate 100,000 users concurrently watching videos and test system performance (such as server response time, crash rate); Through OWASP ZAP scanning interface security vulnerabilities, we finally found system-level flaws such as "abnormal full-screen video playback on Android 13 models" and "response delay of comment function exceeding 5 seconds at 100,000 concurrencies", which pushed the development team to optimize the adaptation and performance architecture.

 

(4) Acceptance Testing: "Final Verification" from the User's Perspective

1. Core Definition: Acceptance testing is a test led by the user or product owner after the system test is passed to verify whether the software meets the user's actual business needs and whether it meets the conditions for launch.

2. Core Goal: Confirm whether the software "meets the real needs of users" rather than just conforming to the requirements document; Collect user feedback to ensure that users can successfully use core functions after launch.

3. Test Object: The entire software system (focusing on the user’s core business processes).

4. Test Timing and Responsible Person: After the system test is completed and before the product is launched; Led by users and product owners, supported by testers.

5. Commonly Used Methods and Types

    ① Alpha testing: In a development environment or internal testing environment, internal users (such as company employees) simulate real usage scenario testing to discover user experience problems in advance;

    ② Beta test: Used by some external users (seed users) in a real user environment to collect feedback in real scenarios (such as ease of use, performance stability);

    ③ Acceptance test-driven development (ATDD): In an agile team, users, products, developers, and testers jointly define acceptance criteria, and design test cases based on the acceptance criteria to ensure that the development process revolves around user needs.

6. Cutting-edge Practice: Optimization of Acceptance Testing under Agile + DevOps

    Traditional acceptance testing is mostly carried out once before going online, with a long cycle and slow feedback. Under the Agile + DevOps model, acceptance testing is "lightweight and high-frequency":

    ① After the end of each iteration cycle (2-4 weeks), conduct a small acceptance test to verify whether the iteration function meets user needs;

    ② Introduce user experience (UX) testing tools (such as Hotjar), collect user operation behavior data (such as click heat maps, dwell time), and assist acceptance testing to determine the usability of functions;

    ③ The acceptance test results are associated with the CI/CD pipeline, and only versions that pass the acceptance can enter the online process.

7. Case

    The acceptance test of an enterprise's OA system adopts the "alpha test + beta test" combined model: first, employees from various departments within the company (administration, finance, human resources) simulate real office scenarios (such as attendance punching, process approval, file upload) to carry out alpha testing, and problems such as "complex configuration of process approval nodes" and "failure to upload large files" were found; After optimization, 10 corporate customers of different sizes were invited to conduct beta tests, and feedback such as "poor adaptation of the mobile approval page" and "low efficiency of multi-department collaborative approval" were collected. After the optimization, it was finally launched and user satisfaction reached 95%.

 

3. Common Test Types: Multi-dimensional Coverage of Software Quality Requirements

In addition to being divided by level, software testing can also be divided into multiple types according to "test objectives", covering multiple quality dimensions such as function, performance, security, and compatibility. Different test types can be cross-applied to each test level (for example, performance testing can be carried out during the system testing phase, or it can be carried out for core interfaces during the integration testing phase).

 

(1) Functional Testing: Verify "What to Do"

- Core Goal: Verify whether the software function meets the requirements and whether it can correctly complete the established business process (such as "user login", "order payment" and "data query").

- Testing Method: Mainly black box testing, focusing on covering normal scenarios, abnormal scenarios, and boundary scenarios.

- Applicable Levels: Unit testing (functional unit verification), integration testing (interface function verification), system testing (overall functional verification), acceptance testing (user core function verification).

- Tools: Selenium (web), Appium (mobile), Cypress (front-end).

 

(2) Performance Testing: Verify "Whether It Is Done Quickly or Stably"

- Core Goal: Evaluate the performance of the software under different loads and discover performance bottlenecks. Mainly include:

  ① Load test: Verify the response time, throughput and other indicators of the system under the expected number of users (such as 10,000 concurrent);

  ② Stress test: Test the stability of the system under expected load (such as 50,000 concurrency) and find the critical point of system collapse;

  ③ Durability test: Verify the stability of the system under long-term operation (such as 72 hours) and whether there are memory leaks, resource exhaustion and other problems.

- Applicable Levels: Integration testing (interface performance), system testing (overall performance).

- Tools: JMeter, LoadRunner, Gatling.

 

(3) Security Testing: Verify "It Is Safe and Unsafe"

- Core Goal: Discover security vulnerabilities in the software and ensure the security of user data and systems.

- Key Coverage: Identity authentication vulnerabilities (such as weak passwords, SQL injection), authorization vulnerabilities (such as unauthorized access), data encryption vulnerabilities (such as sensitive data being stored in plain text), and interface security vulnerabilities (such as unauthorized access).

- Applicable Levels: Integration testing (interface security), system testing (overall security).

- Tools: OWASP ZAP, Nessus, Burp Suite.

- Cutting-edge Trends: AI-driven security testing (such as AI automatically scanning for vulnerabilities and generating penetration test cases).

 

(4) Compatibility Testing: Verify "Whether It Can Be Used in Different Environments"

- Core Goal: Ensure the normal operation of the software in different hardware, software, and network environments. Mainly include:

  ① Hardware compatibility: Different devices (such as PC, mobile phone, tablet), chips (such as Intel, ARM);

  ② Software compatibility: Different operating systems (Windows, macOS, iOS, Android), browsers (Chrome, Edge, Safari), databases (MySQL, Oracle);

  ③ Network compatibility: Different network environments (4G, 5G, Wi-Fi, weak network).

- Applicable Levels: System testing, acceptance testing.

- Tools: BrowserStack, SauceLabs, Charles (weak network simulation).

 

(5) Usability Testing: Verify "Whether It Is Easy to Use and Easy to Learn"

- Core Goal: Evaluate the user experience of the software to ensure that users can quickly understand, learn and use it.

- Focus on: Simplicity of operation steps, rationality of interface layout, clarity of error prompts, and practicality of help documents.

- Applicable Levels: System testing, acceptance testing.

- Methods: User research, eye tracking, usability testing (asking users to complete specified tasks, recording completion time and difficulty).

- Tools: Hotjar, UserTesting.

 

(6) Emerging Test Types: Adapt to Cutting-edge Technology Scenarios

With the development of technology, a variety of targeted test types have been added:

① Cloud native testing: Test elastic scaling, container scheduling, service governance and other capabilities for containerization, microservices, and K8s architecture;

② AI testing: For AI applications, test algorithm accuracy, fairness, explainability, and robustness;

③ In-vehicle testing: For in-vehicle intelligent systems, test real-time performance (such as automatic driving data transmission delay), safety (such as driving control functions), and multi-device interaction (such as linkage between car and mobile phone).

 

4. Collaborative Logic of Test Levels and Types: Full-process Quality Assurance System

In actual testing work, test levels and test types do not exist in isolation, but form a collaborative system of "level progression + type crossover":

1. Progressive Logic: From unit testing to acceptance testing, the scope of testing gradually expands, and the cost of defect discovery gradually increases (so low-level testing needs to be carried out as early as possible);

2. Cross Logic: The same test type can be applied to multiple levels (for example, functional testing covers all levels, and performance testing focuses on integration and system levels);

3. Trend Adaptation: In cutting-edge technology scenarios, it is necessary to add targeted tests based on traditional collaboration logic (for example, cloud native architecture needs to supplement cloud native testing at the integration and system levels, and AI applications need to supplement AI-specific test types at the system and acceptance levels).

 

Example: The testing system of an AI-driven e-commerce APP

- Unit testing: Developers use JUnit to test core functions (such as price calculation, recommendation algorithm units), and the AI tool automatically generates test cases;

- Integration testing: Testers use RestAssured to test microservice interfaces (users, orders, recommendation modules), and use Mock tools to simulate third-party payment interfaces;

- System testing: Use Appium for functional testing, JMeter for performance testing, OWASP ZAP for security testing, and supplement the accuracy and fairness testing of the AI recommendation algorithm;

- Acceptance testing: Carry out α+β testing to collect user feedback on functions and experiences, and verify user recognition of recommended functions.

 

5. Summary: Build a Full-process, Multi-dimensional Test Cognition

The core of software test is "full-process coverage" and "multi-dimensional verification" - the test level ensures layer-by-layer quality filtering from code to users, and the test type ensures that all quality requirements such as function, performance, and security are covered. Mastering the progressive logic of test levels and application scenarios of test types is the basis for scientific testing work.

 

With the popularization of technological trends such as AI, cloud native, and agile, testing work needs to break traditional boundaries and realize the transformation of "shifting testing left (intervening in development earlier)", "shifting testing right (focusing on the production environment)", and "human-machine collaboration (improving testing efficiency)".

 

In the next article, we will focus on "Testing in the Software Development Life Cycle", explain in detail the differences and practical points of testing work under waterfall, agile and DevOps modes, and help everyone adapt to the testing needs of different development modes.

(From: TesterHome)