Foreword

In 2026, AI Agent 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 their 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. What is Software Testing?

Today, as the wave of digitalization sweeps the world, software has penetrated into various fields such as life, work, and industry. From mobile banking APPs to vehicle-mounted intelligent systems, from e-commerce platforms to industrial control systems, software quality is directly related to user experience, business security, and even the safety of life and property.

Software testing is the core link to ensure software quality. Its official definition can refer to the IEEE standard: the process of discovering defects in requirements, design, and code by executing programs or analyzing systems, verifying whether the software meets expected requirements, and evaluating its non-functional attributes (performance, security, ease of use, etc.).

Many beginners simply equate software testing to "finding bugs", but this is only one of the core goals of testing. Complete software testing also includes:

- Confirming the correctness and completeness of software functions;

- Evaluating whether software performance meets business requirements;

- Verifying system compatibility and stability;

- Ensuring data security and compliance.

More importantly, modern software testing has shifted from "post-event checking" to "full-process prevention". Through strategies such as shifting testing left (intervention in the demand stage) and shifting testing right (production environment monitoring), quality risks can be avoided and the cost of repairing defects can be reduced throughout the software life cycle.

2. The Core Value of Software Testing

In the industrial context of deep empowerment of "artificial intelligence +", the speed of software iteration continues to accelerate, and users' requirements for quality are also getting higher and higher. The value of software testing is mainly reflected in three dimensions:

(1) Reduce Business Risks

Avoid economic losses or reputational damage caused by software defects. For example, defects in the transfer function of the financial system may cause financial losses, and faults in vehicle software may endanger driving safety. Complete testing can detect such high-risk problems in advance.

(2) Improve User Experience

Through compatibility testing, usability testing, etc., ensure that the software can run stably on different devices and in different scenarios and conform to user usage habits. In a market with fierce homogeneity and competition, high-quality user experience is often the key for a product to stand out.

(3) Ensure Iteration Efficiency

Under agile development and DevOps models, test automation and continuous testing have become the basis for rapid delivery. Through the integration of automated testing tools and CI/CD pipelines, testing can be automatically triggered after code submission, significantly shortening the iteration cycle without reducing quality.

The importance of testing positions can also be seen from industry recruitment data. Whether it is vehicle-mounted software testing, cloud testing related positions, or traditional software testing engineers, they all require a solid testing foundation and quality control awareness, and the salary level is also in the middle and upper reaches of the industry.

3. Core Principles of Software Testing

No matter how the testing technology iterates, some core principles are always the guiding direction of testing work. Combined with the latest technology trends, they can be summarized into the following six points:

(1) Test as Early as Possible and Participate Throughout the Process

Testing should not be limited to after the development is completed, but should intervene during the requirements stage, participate in requirements review, and identify requirement ambiguities or omissions in advance. In the AI-driven testing mode, natural language parsing tools can even be used to initially identify test points during the requirements document generation stage.

(2) Exhaustive Testing is Not Feasible; Risk-Driven Testing

The input and scenario combinations of the software are unlimited, and exhaustive testing cannot be achieved. Therefore, it is necessary to prioritize testing through risk assessment and focus on high-risk modules (such as core transaction processes and high-frequency use functions). This principle also applies to AI testing: mounting the enterprise's historical bug library through RAG technology can more accurately locate high-risk areas.

(3) Defect Clustering Effect

80% of defects are often concentrated in 20% of modules. This classic law still holds true in modern software. During the testing process, when multiple defects are found in a certain module, the testing intensity of the module should be increased, and if necessary, static code analysis tools should be used for in-depth investigation.

(4) Independence and Objectivity of Testing

Testers should be independent from developers and maintain an objective perspective of judgment. In team collaboration, the model of "development self-testing + testing team verification" can be used to balance efficiency and objectivity.

(5) Testing Should Trace Requirements

All test cases should be traceable to specific demand points to ensure full coverage of requirements. In the intelligent testing platform, real-time statistics and visualization of test coverage can be achieved through automatic association of requirements and use cases.

(6) Human-Machine Collaboration and Complementary Advantages

AI can efficiently complete repetitive testing work (such as use case generation, regression testing), but logical verification of complex scenarios, user experience evaluation, etc. still require manual intervention. Testers should focus on core tasks such as quality strategy design and complex scenario mining, rather than tedious script writing.

4. Changes in Software Testing in the Intelligent Era

With the development of AIAgent, cloud native, DevOps and other technologies, software testing is undergoing a transformation from "human-led" to "human-machine collaboration", which is mainly reflected in three aspects:

(1) Qualitative Changes in Testing Efficiency

AI-driven testing tools can realize natural language generation test cases, visual self-healing automation scripts and other functions, significantly lowering the testing threshold. For example, Ctrip's AI use case generation platform has increased the efficiency of use case generation by 70% for small and medium-sized needs, and by 50% for large-scale needs.

(2) Expansion of Testing Scope

The distributed architecture of cloud-native environments, the black-box logic of AI applications, and the real-time requirements of in-vehicle software all pose new challenges to testing. Testers need to master emerging technologies such as containerized testing, chaos testing, and multi-modal testing.

(3) Upgrading of Testing Roles

Traditional "functional testers" are transforming into "quality architects" and need to have more comprehensive technical capabilities, including automation framework design, AI testing strategy formulation, full-process quality control, etc.

5. Getting Started with Software Testing: Establish "Quality Thinking"

The core of getting started with software testing is to establish "quality thinking". First, master the basic definitions, principles and processes, and then gradually learn the test design methods and tool usage.

In the era of intelligence, beginners do not need to be afraid of technological changes. They should regard trends such as AI and cloud native as directions for learning, not obstacles. Subsequent articles will gradually explain in depth the core testing process, use case design methods, tool practices, etc. to help you build a systematic testing knowledge system and calmly respond to industry development needs.

(From: TesterHome)