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. In contrast, AI-driven testing solutions have achieved several-fold efficiency improvements and have become the core choice for quality assurance in industries such as finance and automotive.

Facing the new paradigm of "human-machine collaboration", whether you are a novice looking to get started or a practitioner seeking skill upgrading, you need a knowledge system that balances basic logic and cutting-edge trends.

To this end, the TesterHome Community has launched a series of articles titled "Advanced Testing Quality". Starting from the core understanding of testing, it gradually delves 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 analysis, it helps everyone build testing capabilities adapted to industry changes. This series will be continuously updated, so stay tuned!

Introduction: Test Cases are the "Core Carrier" of Quality Verification

Test cases are the foundation for test execution and directly determine test coverage, defect discovery efficiency, and the accuracy of quality verification. High-quality test cases can comprehensively cover business scenarios and quality risks, avoiding "blind testing"; on the contrary, non-standard and incomplete use cases will lead to defect leakage and bury online quality risks.

Entering the intelligent era, software iterations are accelerating and the penetration rate of AI functions is increasing. The traditional use case design model of "manually sorting through scenarios one by one" faces challenges such as "low efficiency, insufficient coverage of complex scenarios, and difficulty in disassembling AI black-box functions". According to Tricentis' 2026 QA Trend Report, more than 40% of code will be generated by AI in 2025, but 88% of respondents lack confidence in deploying AI-generated code, and 29% of companies were forced to roll back releases due to AI code errors.

This current situation highlights the importance of high-quality test cases, but it does not mean that traditional methods should be abandoned—classic methods such as equivalence classes, boundary values, and scenario-based approaches are still the core logic of use case design, while AI technology is an important tool to improve efficiency and break through the bottleneck of complex scenario design. This article will systematically dissect the practical key points of traditional use case design methods, detail the practical path of AI-enabled use case generation and optimization, help testers achieve the efficient integration of "traditional logic + AI tools", and improve the quality and efficiency of use case design.

1. Lay a Solid Foundation: Practical Key Points of Traditional Test Case Design Methods

Traditional test case design methods have been verified in practice for many years. Their core value lies in "accurately disassembling requirements and covering core risks", which is the logical basis for AI use case generation. The following are the four most commonly used methods, combined with practical steps and case descriptions:

(1) Equivalence Partitioning Method: Efficiently Reduce the Scope of Testing

1. Core Logic: Divide the input/output data in the requirements into "equivalence classes" (i.e., data sets with the same characteristics), and select representative data from each equivalence class as test cases—as long as the representative data passes the test, it can be inferred that other data in this class meet the requirements, thereby reducing the number of use cases and improving testing efficiency.

2. Classification Types:

   • Valid equivalence class: Legal data that meets the requirement specifications (e.g., the valid equivalence class for "mobile phone number input" is "11 digits, starting with 13/14/15/17/18/19");

   • Invalid equivalence class: Illegal data that does not meet the requirement specifications (e.g., the invalid equivalence classes for mobile phone numbers are "10 digits", "contains letters", and "null value").

3. Practical Steps:

   1. Sort out the input/output conditions in the requirements;

   2. Divide valid and invalid equivalence classes and clarify the boundaries of each equivalence class;

   3. Design 1-2 representative test cases for each equivalence class.

4. Practical Case: The "password reset" function requirement of an e-commerce APP: "The password should be 6-12 characters long, including letters + numbers."

   • Valid equivalence classes: 6 characters (letters + numbers), 12 characters (letters + numbers), 8 characters (letters + numbers);

   • Invalid equivalence classes: 5 characters (insufficient length), 13 characters (exceeding length), pure numbers, pure letters, special symbols, and null values;

   • Test cases: Input "a12345" (valid, 6 characters), "a123456789012" (valid, 12 characters), "12345" (invalid, 5 characters), "abcdef" (invalid, pure letters).

(2) Boundary Value Analysis Method: Focus on High-Risk Boundaries

1. Core Logic: Software defects are mostly concentrated at the "boundaries" of data ranges (e.g., 5, 6, 12, and 13 characters for "6-12-character passwords"). By focusing on testing boundary values and values near the boundaries, boundary-related defects can be discovered efficiently. This method is often used in conjunction with the equivalence partitioning method.

2. Practical Steps:

   1. Extract boundary conditions (such as length range, numerical range, time node) from the equivalence class division results;

   2. Design test cases for boundary values (minimum value, maximum value) and values near the boundaries (minimum value - 1, maximum value + 1);

   3. Prioritize covering the connection points between "valid boundaries" and "invalid boundaries".

3. Practical Case: Continue with the "password reset" requirement (6-12 characters, letters + numbers).

   • Minimum value boundary: 6 characters (valid, "a12345"), 5 characters (invalid, "a1234");

   • Maximum value boundary: 12 characters (valid, "a123456789012"), 13 characters (invalid, "a1234567890123");

   • Intermediate value around the boundary: 8 characters (valid, "a1234567")—verifies the compatibility of data within the boundaries.

(3) Scenario-Based Method: Cover the Entire Process of Business Logic

1. Core Logic: Simulate the real usage "scenarios" of users, sort out "normal scenarios" and "abnormal scenarios" in the business process, design use cases according to the process steps, and ensure the logical correctness of covering the user's entire operation link. Suitable for test case design of complex business processes (such as shopping, money transfer, ordering).

2. Practical Steps:

   1. Sort out the core steps of the business process (e.g., "e-commerce ordering": browse products → add to cart → submit order → select payment method → payment completed);

   2. Identify normal paths (no exceptions) and abnormal paths in the process (such as out-of-stock products, payment failure, and empty address);

   3. Design a complete process-based use case for each scenario, clarifying the input/output and expected results of each step.

3. Practical Case: Use case design for the "order and pay" function of a food delivery APP:

   • Normal scenario: Browse merchants → select products → add to cart → submit order (fill in address) → select WeChat Pay → payment successful → order generated;

   • Abnormal scenario 1: Empty address when submitting the order → prompt "Please fill in the shipping address";

   • Abnormal scenario 2: Insufficient balance when paying → prompt "Insufficient balance, please change the payment method";

   • Abnormal scenario 3: Products are out of stock after submitting the order → prompt "Some products have been sold out, do you want to continue placing the order?"
     Each scenario use case must include "steps + inputs + expected results" to ensure process integrity.

(4) Error Guessing Method: Predict Defects Based on Experience

1. Core Logic: Based on testers' project experience and industry common sense, predict the types of errors that may occur in the software (such as input spaces, special symbols, repeated submissions, network interruptions), and design targeted use cases. This method has no fixed process and relies on testers' experience accumulation, serving as a supplement to other methods.

2. Common Prediction Directions:

   • Input exceptions: Spaces, null values, special symbols, values beyond a reasonable range (e.g., a negative order amount);

   • Operation exceptions: Repeated submissions, continuous button clicks, recovery after network interruption, background service restart;

   • Data exceptions: Duplicate data, missing data, incorrect data format (e.g., the date format is "2026/13/01").

3. Practical Case: Error guessing for the "money transfer" function of a financial APP

   • Test case 1: Enter a negative transfer amount → prompt "The amount cannot be negative";

   • Test case 2: Continuously click the "Confirm Transfer" button → only perform one transfer to avoid repeated deductions;

   • Test case 3: Network interruption during the transfer → prompt "Network abnormality, transfer status is unknown, please check later", and the balance will not be deducted;

   • Test case 4: Enter a payee account number containing spaces → automatically remove the spaces or prompt "Account format error".

2. Intelligent Empowerment: AI-Driven Test Case Generation and Optimization

Traditional methods have limitations such as "low design efficiency and incomplete coverage of complex scenarios" in complex business scenarios (e.g., AI recommendation, multi-module linkage) and high-frequency iteration requirements. By empowering AI technology throughout the entire process of "requirement analysis → scenario generation → use case optimization", the efficiency of use case design can be greatly improved, and the design bottleneck of complex scenarios can be broken through.

(1) Core Principles and Tool Selection of AI-Generated Test Cases

1. Core Principles

   • AI tools analyze requirement documents (PRD, design documents) based on NLP technology and extract core function points, input/output conditions, and business processes;

   • Combined with machine learning algorithms (such as transfer learning based on historical use case libraries, generative AI based on business rules), test cases covering equivalence classes, boundary values, and scenario-based methods are automatically generated;

   • Finally, semantic analysis is used to optimize the standardization and executability of the use cases.

2. Mainstream Tool Selection

   • Commercial tools: Testim (supports automatic generation and maintenance of functional use cases), Mabl (focuses on end-to-end scenario use case generation), Autify (codeless AI use case generation, suitable for non-technical personnel);

   • Open-source tools: CodiumAI (focused on unit testing/interface test case generation, supports multiple languages), GenAI-Test (use case generation tool based on large models, customizable);

   • Localization solution: Ollama+LangChain (connected to private large models, adapted to scenarios with high data privacy requirements, such as finance and medical care);

   • Domestic tools: Tencent WeTest (supports automatic generation of mobile use cases), Alibaba Cloud Effective Test Platform (connects with the Alibaba ecosystem, supports cloud-native application use case generation).

3. 2025-2026 Tool Trend Update

Current AI testing tools have shown two major trends: "large model native application + full-link automation":

• Internationally: Large models such as Grok, Claude 3.5/Opus, and Gemini 1.5 Pro have become the main force in use case generation, and LangChain can quickly realize requirement document analysis and customized use case generation. Many teams have abandoned special tools and turned to the "large model + lightweight plug-in" model; TestRigor has risen with its full-link capabilities of natural language generation of use cases + automatic execution, supporting Web/mobile/API multi-terminal testing; Applitools Eyes has added AI-enhanced visual testing features, which can automatically identify pixel-level differences and interaction logic anomalies.

• Domestically: Baidu Comate and Huawei Pangu Test Assistants are deeply integrated with the R&D process and support the automatic generation of use cases for low-code platforms; ByteDance Doubao has launched a special plug-in for testing, which can directly generate use cases that comply with domestic business specifications based on PRD and supports automatic defect association with use cases. In addition, tools such as Keploy and Diffblue Cover continue to iterate in the field of API/unit testing to achieve real-time synchronous updates of test cases and code.

(2) Practical Process of AI-Enabled Test Case Design

AI-generated use cases are not "ready to use with one click". They need to be combined with manual verification and optimization to form a closed-loop process of "AI generation → manual screening → supplementary optimization → implementation". The specific steps are as follows:

Step 1: Requirement Preprocessing and Prompt Design

1. Preprocess requirement documents: Organize clear PRD documents (remove redundant information, clarify function points and constraints);

2. Design accurate prompts: Clarify the "requirement content + use case type + coverage requirements + output format" to the AI tool. The following are 3 complete prompt templates that can be directly reused, adapting to different test scenarios:

   Template 1 (Equivalence Class + Boundary Value Dedicated):
   "Generate functional test cases based on the following requirements, and strictly follow the equivalence partitioning method and boundary value analysis method. Requirement: A certain mini-program's 'mobile phone number verification code login' function supports the input of 11-digit mobile phone numbers in mainland China. The verification code is 6 digits and valid for 5 minutes. Requirements: 1. Divide valid/invalid equivalence classes (mobile phone number format, verification code format/validity period); 2. Cover all boundary values (mobile phone number: 10 digits/11 digits/12 digits, verification code: 5 digits/6 digits/7 digits); 3. Output format: Use case ID - Test purpose - Input data - Expected results - Preconditions"

   Template 2 (Scenario-Based Method Dedicated):
   "Generate end-to-end test cases based on the following business processes, which need to cover normal scenarios and all key abnormal scenarios. Business process: The 'make an appointment and place an order' process in a certain food delivery app (browse merchants → select products → add to cart → confirm appointment time → submit order → pay). Requirements: 1. Sort out the complete process steps; 2. Identify abnormal points in each step (such as out-of-stock products, expired appointment time, payment failure); 3. Each use case contains 'step sequence - input data - expected results - exception triggering conditions'; 4. Output format: Use case ID - Scenario type - Detailed steps - Input - Expected results"

   Template 3 (AI Recommendation System Special):
   "Generate test cases for the AI recommendation function of an e-commerce APP, which need to cover data distribution equivalence classes, robustness, bias, and fairness tests. Requirements: Recommend products based on users' historical browsing/purchasing behavior, with a recommendation accuracy of ≥85% and a recommendation deviation of ≤5% for different groups. Requirements: 1. Divide equivalence classes according to user characteristics (age/gender/spending power); 2. Include robust scenarios such as keyword typos and malicious clicks; 3. Verify gender/regional bias (differences in recommendations for users of different genders/regions with the same needs); 4. Output format: Use case ID - Test dimensions - Input data (user characteristics/behavior) - Expected results (quantitative indicators + qualitative description)."

   Accurate prompts can greatly improve the accuracy of AI-generated use cases.

Step 2: AI Automatically Generates Use Cases

Input the preprocessed requirements and prompts into the AI tool to generate an initial set of use cases. For example, in response to the requirement of "ordering with limited-time discounts", AI can automatically generate dozens of use cases, such as "ordering during the effective discount period", "ordering beyond the discount period", "ordering for out-of-stock discount products", and "ordering for batch discount products", covering core scenarios and boundary conditions.

Step 3: Manual Screening and Supplementary Optimization

1. Screen invalid use cases: Eliminate duplicate use cases generated by AI and use cases that do not conform to business logic (e.g., AI may generate use cases with "a negative discount amount". If the requirement clearly states that the discount amount ≥ 0, it needs to be eliminated);

2. Supplement complex scenarios: Edge scenarios that are difficult for AI to cover and cross-module linkage scenarios (such as "limited-time discounts + superimposed use of coupons" and "cross-time zone users participating in limited-time activities") need to be manually supplemented by testers based on traditional methods;

3. Optimize the standardization of use cases: Unify the use case format, improve the details of test steps (such as clarifying the "network environment" and "test environment"), and ensure that the use cases are executable.

Step 4: Use Case Review and Iteration

Organize product, development, and testing teams to review the optimized use case set and confirm the use case coverage and the rationality of expected results; if requirements change, repeat the above process and quickly iterate the use cases through AI (e.g., if the requirement is to add "a limit of 10 items for discounted products", AI can quickly generate boundary value use cases of "buying 10 items" and "buying 11 items").

(3) Integration Strategy of Traditional Methods and AI: 1+1>2

AI does not replace traditional methods but complements them. The core integration strategies are as follows:

1. Use traditional methods to guide AI generation logic: Integrate the logic of traditional methods such as equivalence classes and boundary values into prompts to make AI-generated use cases more relevant to quality risk points. For example, clearly state in the prompt that "the boundary values of password length need to be covered (6 characters, 12 characters, 5 characters, 13 characters)" to guide AI to focus on core boundaries;

2. Use AI to break through the efficiency bottlenecks of traditional methods: For simple and repetitive scenarios (such as login and registration) and high-frequency iteration requirements (such as e-commerce activity iterations), use AI to quickly generate basic use cases, and testers focus on supplementing complex scenarios to improve overall design efficiency. For example, an e-commerce APP iterates its activity functions twice a month, and generating basic use cases through AI can save 60% of design time; Gartner research data shows that teams adopting AI-assisted use case design in 2025 will shorten the average test preparation cycle by 45% and increase use case coverage by 30%;

3. Use traditional methods to verify the quality of AI use cases: Use traditional standards such as equivalence class coverage, boundary value integrity, and scenario process rationality to verify the quality of AI-generated use cases, avoiding AI missing core risk points. For example, use the scenario-based method to sort out the entire process and check whether the use cases generated by AI cover the complete user operation link.

(4) Special Use Case Design for AI Functions: Addressing Black Boxes and Non-Determinism

For AI-driven functions (such as recommendation systems, intelligent customer service, and AI risk control), their black-box characteristics and non-deterministic output bring special testing challenges. It is necessary to combine "traditional methods + AI-specific strategies" to design use cases. The core points are as follows:

1. Equivalence class division based on data distribution: Divide AI input data into equivalence classes according to "feature distribution" (such as user age, behavioral preferences, input text type), and test the consistency of AI output under different data distributions. For example, the user equivalence classes of an AI recommendation system are: "Young women - beauty preferences", "Middle-aged men - digital preferences", "Elderly users - daily necessities preferences" to test the recommendation accuracy of different types of users;

2. Robustness and adversarial use case design:

   • Robustness use cases: Input small perturbation data (such as keyword typos in intelligent search, image noise of recommended products) and test the stability of AI output;

   • Adversarial use cases: Generate targeted adversarial samples (such as adding specific watermarks to product images to test whether the recommendation system misjudges product categories) and test AI's anti-attack capabilities;

3. Explainable use case design: AI functions in highly regulated industries need to verify the interpretability of decisions. When designing use cases, the "traceability of AI decision-making basis" needs to be clear. For example, a use case for a financial AI risk control system: "User A was denied a loan due to a 'debt ratio of 75%'. It is necessary to verify that the system can clearly output the reason for the rejection, and that the reason is consistent with the user's actual data.";

4. Design use cases related to data drift: Simulate data drift scenarios that may occur in the production environment (such as changes in user behavior preferences, changes in product attribute distribution), and design use cases to test the performance attenuation of the AI model. For example, a use case for an e-commerce recommendation system: "Input 30% of new types of product data and test whether the recommendation accuracy drops by more than 10%.";

5. Metamorphic Testing: Aimed at the non-deterministic output characteristics of AI functions, it does not rely on specific expected results. Instead, it verifies whether the output meets the preset relationship by designing input transformation rules. For example, in an e-commerce recommendation system, design input transformations such as "rotating the order of recommended products" and "adding or subtracting 2 similar products" to verify that the recommendation accuracy does not fluctuate by more than 5% after the transformation; in the AI translation function, test the semantic consistency of the translation after "original synonym replacement". This method can effectively solve the problem that non-deterministic AI function testing is difficult to quantify;

6. Bias and fairness testing: Focus on core scenarios such as recommendation and risk control to verify whether there is gender, age, or geographical bias in AI decision-making. For example, in a financial AI risk control system, design the use case "Users of different genders but with exactly the same credit report and debt ratio apply for a loan" to verify that the difference in approval rate is ≤3%; in an e-commerce recommendation system, test whether the geographical distribution deviation of recommended products is reasonable when users from different regions search for the same keyword to avoid regionally discriminatory recommendations. Highly regulated industries need to incorporate fairness indicators into test acceptance criteria.

3. Practical Cases: Use Case Design Practice in Different Scenarios

(1) Case 1: Traditional E-Commerce Ordering Function (Agile Iteration Scenario)

• Requirements: Support single/batch product orders, WeChat/Alipay can be selected as the payment method, the order amount is ≥0.01 yuan, and prompt "Product has been sold out" when inventory is insufficient.

• Design Process:

  1. Use the AI tool (Testim) to input requirements and generate basic use cases (covering single product orders, batch orders, different payment methods, and amount boundary values);

  2. Testers use the scenario-based method to supplement complex scenarios such as "network interruption after placing an order", "canceling the order after payment", and "merging orders for cross-store products";

  3. Use the error guessing method to supplement abnormal use cases such as "repeated order submission" and "order amount is 0";

  4. Finally, 68 use cases are formed, covering all scenarios, and the design efficiency is increased by 50% compared with pure manual work.

(2) Case 2: Financial AI Risk Control System (Highly Regulated Scenario)

• Requirements: Automatically approve loan applications based on user credit information, debt ratio, income level, and other data, with an approval accuracy rate of ≥98%, a misjudgment rate of ≤0.5%, and supported explainable decision-making.

• Design Process:

  1. Use Ollama+LangChain to build a self-developed AI use case generation tool (to ensure data privacy), divide equivalence classes based on user data characteristics, and generate basic use cases;

  2. Testers supplement robustness use cases (such as minor errors in user credit data), adversarial use cases (such as forging part of the credit data), and explainability use cases (such as verifying that the reasons for loan rejection can be traced);

  3. Use the boundary value method to design boundary use cases such as "debt ratio of 70% (critical value)" and "income level just meeting the standard";

  4. Finally, 120 use cases are formed, covering core requirements such as accuracy, robustness, and interpretability. After going online, the defect leakage rate is ≤0.3%.

(3) Case 3: AI E-Commerce Recommendation Function (Intelligent Scenario)

• Requirements: Recommend products based on users' historical browsing, collection, and purchase behaviors, with a recommendation accuracy of ≥85% and a recommendation deviation of ≤5% for different user groups.

• Design Process:

  1. Divide equivalence classes according to user characteristics (age, gender, spending power), and generate recommendation test cases for different groups;

  2. Supplement robustness use cases (such as typos in product names in users' browsing history) and adversarial use cases (such as malicious clicks on certain products);

  3. Design data drift use cases (such as users' recent behavior shifting from "clothing" to "home appliances");

  4. Use A/B test cases to verify the effects of different recommendation algorithms;

  5. Finally, 95 use cases are formed, covering core indicators such as recommendation quality, fairness, and robustness.

(4) Case 4: WeChat Mini Program Form Submission Function (Pure Front-End Scenario)

• Requirements: The WeChat Mini Program user information form supports the submission of name (2-8 Chinese characters), mobile phone number (11 digits), and address (non-empty). If the submission is successful, it will jump to the result page and provide real-time prompts for format errors.

• Design Process:

  1. Use the ByteDance Doubao testing plug-in to input requirements and generate basic use cases (covering valid/invalid equivalence classes and boundary values of name/mobile phone number);

  2. Testers use the scenario-based method to supplement front-end special scenarios such as "data retention when exiting after filling in half of the form", "submission in a weak network environment", and "compatibility of different WeChat versions";

  3. Use the error guessing method to supplement abnormal use cases such as "name contains special symbols", "mobile phone number contains spaces", and "repeated submission";

  4. Combine with the Applitools Eyes AI visual testing tool to add new visual use cases (to verify the consistency of form layout and the uniformity of error prompt styles on different models);

  5. Finally, 42 use cases are formed, covering functional correctness and front-end compatibility. After going online, the front-end defect leakage rate is ≤0.2%.

4. Frequently Asked Questions and Pitfall Avoidance Guide

Question 1: AI-Generated Use Cases Do Not Fit the Actual Business
Pitfall Avoidance Guide:

1. Optimize prompts and clarify business rules and constraints (e.g., "need to comply with the e-commerce platform's '7-day no-reason return' rule");

2. Preprocess requirement documents and supplement business scenario descriptions (e.g., "Batch orders support up to 10 items");

3. Establish a "use case template library" to allow AI to generate use cases based on templates that fit the business.

Question 2: Incomplete Coverage of Use Cases in Complex Scenarios
Pitfall Avoidance Guide:

1. Combine "scenario-based method + AI generation": first use the scenario-based method to sort out the entire process, then let AI supplement the detailed use cases in the process;

2. Organize cross-role reviews (product, development, testing) to identify missing scenarios from different perspectives;

3. Refer to the use case library of historical projects to reuse use cases for similar scenarios.

Question 3: Too Many Use Cases and Low Execution Efficiency
Pitfall Avoidance Guide:

1. Classify use cases according to risk priority (P0 core use cases, P1 secondary use cases, P2 low-risk use cases), and prioritize P0/P1 use cases in iterative testing;

2. Merge highly repetitive use cases (e.g., "Ordering use cases for different payment methods" can be merged into "Payment method traversal test cases");

3. Convert frequently executed use cases into automated scripts to improve execution efficiency.

Question 4: Difficulty in Quantifying Expected Results of AI Function Use Cases
Pitfall Avoidance Guide:

1. Clarify the quantitative indicators of AI functions (e.g., recommendation accuracy ≥85%), and use the indicators as expected results;

2. For non-deterministic output, set an "acceptable range" (e.g., recommendation deviation for different user groups ≤5%);

3. Use AI evaluation tools (such as Evidently AI) to automatically verify expected results and reduce manual judgment errors.

Question 5: Frequent Requirement Changes and High Use Case Iteration Costs
Pitfall Avoidance Guide:

1. Use AI tools to quickly iterate use cases (e.g., if the requirement is to add "coupon superimposed discount", AI can quickly generate related use cases);

2. Adopt "modular use case design" to separate the core process from the variable process. When requirements change, only modify the use cases of the variable modules;

3. Establish an automatic traceability relationship between requirements and use cases, and automatically mark affected use cases after requirements change.

Summary

The core of use case design is "accurate coverage + efficient iteration". The core goal of test case design is to "accurately cover quality risks and support efficient test execution". Traditional methods are the logical basis for use case design, ensuring that use cases meet business needs and quality risks; AI technology is the key to improving efficiency, helping testers break through design bottlenecks in complex scenarios and high-frequency iterations. The integration of the two is not a "replacement" but a "complementarity"—only by using traditional methods to control the quality of use cases and AI tools to improve design efficiency can we adapt to the development characteristics of software in the intelligent era.

Excellent test cases should not only cover "normal scenarios" but also accurately target "abnormal scenarios and boundary risks"; they must not only be "executable" but also "iterable and reusable". As a tester, you need to be proficient in the core logic of traditional use case design methods and at the same time learn to use AI tools to optimize the design process, avoiding falling into the misunderstandings of "pure manual inefficiency" or "pure AI inaccuracy". It is recommended that you try to use a large model to generate a use case for your own project and compare the time and coverage with traditional methods.

In the next article, we will focus on "test execution and defect management", detailing the automated test execution strategy, intelligent defect positioning, and closed-loop management methods in the intelligent era, helping everyone get through the "last mile" of the entire testing process and achieve efficient implementation of quality verification.

Source: TesterHome Community