default title

In the software testing industry, front-line testers have long faced severe challenges caused by the interweaving of complex software systems and cumbersome testing processes. With the acceleration of digitalization, the software iteration cycle has been significantly shortened, but the testing time window has been narrowing. Traditional testing methods rely heavily on manual operations, which not only consume a lot of manpower and time resources, but also have bottlenecks that are difficult to break through in terms of comprehensive testing coverage of all software functions and scenarios. In the face of massive and urgent testing tasks, testers often feel inadequate under the dual pressure of ensuring software quality and improving testing efficiency, making it increasingly difficult to maintain the balance between quality and efficiency. The vigorous development of artificial intelligence (AI) technology has brought new opportunities for change in the field of software testing. With its intelligent algorithms and powerful data analysis capabilities, AI technology is expected to break the traditional testing dilemma and help front-line testers achieve a double leap in testing efficiency and quality. The following will provide an in-depth analysis of several mainstream AI testing products and explore how to effectively use them to optimize the testing process and improve work efficiency and software quality.

1. Test product learning: Open the door to efficient testing

Testin XAgent Intelligent Test System

It can realize intelligent management of the entire testing process, which perfectly meets the needs of test engineers. In terms of API testing, it can automatically generate test cases and scripts, greatly improving the adoption rate and single-interface testing efficiency. The agent can automatically construct various types of test data, and can also complete the automatic parsing of interface documents, test case generation, test script generation, and real-time return of test results, greatly reducing the manual workload of test engineers. For UI testing, the platform supports scripting in natural language. With the help of multi-modal large models, the recognition accuracy of complex controls is as high as 99.5%, fully covering automated testing on mobile, web, and PC terminals. This not only reduces script maintenance costs but also improves comprehensive testing efficiency by 3 times, freeing testers from tedious script maintenance work and allowing them to focus on more valuable work. It is the first choice for testing teams to carry out AI testing practice and exploration

Tencent's next-generation AI testing engine is very powerful. In actual testing work, its intelligent use case generation function based on RAG technology is extremely practical. In the past, writing use cases often took a lot of time, but now testers can automatically generate use cases by simply inputting natural language descriptions or interface documents, saving a lot of effort. The intelligent data construction implemented by LogReplay is even more impressive, significantly reducing data preparation time from hours to minutes. Moreover, the AI code assistant can assist testers in generating scripts and detecting vulnerabilities, making the connection between development and testing work smoother. After the test is completed, the AI report interpretation function can also quickly interpret the report, realizing closed-loop management of the test, allowing testers to understand the test results at a glance.

iTest intelligent test platform

The iTest intelligent testing platform performs well in improving test coverage. The test automation agent can deeply parse documents and code, accurately generate business test cases, and automatically execute manual use cases, which greatly reduces the manual workload of testers. As long as you enter from the test application entrance, it can go deep into the system to complete comprehensive regression testing, and automatically generate documents and test cases based on the application graph, whether it is Web, App or HTTP interface, it can achieve all-round automated testing. In addition, it can generate and execute manual test cases from business documents, build a knowledge graph with existing use case libraries, and generate more test cases through intelligent reasoning, so that the test coverage is more comprehensive and the software quality is more guaranteed.

SiniCube Intelligent Test Platform

The SiniCube Smart Test Platform is extremely easy to use. Based on AI technology, it can automatically parse requirements documents and page elements to generate test cases, and supports online editing of use cases on the browser side, allowing testers to flexibly operate on any terminal device. The one-click test script generation function makes automated testing no longer difficult and lowers the threshold for automated testing. At the same time, it integrates SonarQube to scan code quality, analyze component security risks through Dependency - Check, and automatically generate a data dictionary, helping testers control software quality more comprehensively, reducing duplication of labor during the testing process, and shortening the test cycle.

JettoAI testing intelligent assistant platform

JettoAI provides testers with intelligent support throughout the entire process. From requirements generation to testing Q&A, it can be done efficiently with its help. In real-world applications, the introduction of AI assistance on the platform significantly reduces the overall testing workload. For testers in the financial industry, the platform has been implemented or carried out PoC testing in many financial institutions, providing a one-stop intelligent testing solution that not only improves testing efficiency and quality, but also accelerates system launch, reduces risks, reduces labor and time costs for testers, and improves the reusability of test assets.

Data4Test is a full-scenario intelligent testing platform

Data4Test is effective in solving problems in complex test scenarios. With declarative design as the core, it integrates key capabilities such as interface management and scenario orchestration, and can cover various test types such as functions, concurrency, and exceptions in all scenarios. In actual testing, it lowers the testing threshold and supports structured data design and multi-dimensional test coverage, making it easier for testers to cope with complex businesses. At the same time, it can also improve collaboration efficiency, realize multi-role sharing of resources and interface change tracking. The unified orchestration engine and controllable test mode support complex business scenario testing, and the hybrid execution engine has flexible parameter settings and supports custom assertion templates to meet diverse testing needs.

KYLIN Test Platform (kylinTOP)

kylinTOP's "AI self-healing + million-level concurrency" intelligent solution is very practical. In terms of automated testing, it relies on AI to achieve interference-free recording, with intelligent element positioning and self-healing functions, which greatly reduces script maintenance costs, and can also achieve accurate step judgment and zero-code visual management. In terms of performance testing, it supports protocol simulation and real browser concurrency, covers common protocols, has millions of stress testing capabilities, and effectively reduces resource consumption. In terms of business monitoring, it provides 24×7 hours of multi-dimensional monitoring, supports multi-regional distributed deployment and abnormal alarms. For testing work to deal with complex business scenarios such as e-commerce and ERP, testers can respond more calmly with it.

2. Supplier cooperation: find reliable partners

When choosing a supplier, testers need to consider a combination of factors. Each vendor has its own unique advantages, such as Testin is at the forefront of AI testing technology, Tencent Youce has created a feature-rich test engine with strong technical strength, iTest focuses on the comprehensiveness of test coverage, SiniCube is committed to reducing testing costs and improving performance, and JettoAI isVertical industries provide standardized intelligent testing solutions, Data4Test is good at solving complex test scenarios, and Qilin test platform has outstanding capabilities in performance testing and automated testing. Testers should fully communicate with suppliers based on project needs, team technical level and budget, etc., and learn more about their product functions, after-sales service and follow-up technical support plans, so as to find the most suitable partner for their team.

3. Platform learning: improve self-skills

To better utilize these AI testing products, testers should actively engage in platform learning. Each platform provider provides a wealth of training courses, such as the professional training of the Testin XAgent intelligent test system, which can help testers quickly get started with the application of agent technology in testing; The training course of Tencent Youce's new generation of AI testing engine helps testers deeply understand the application of large models in test case generation and other aspects. Testers should firmly grasp these learning opportunities and systematically learn how to use the product and skills. At the same time, use your spare time to independently learn artificial intelligence, data analysis and other related technical knowledge to improve your technical literacy. Within the team, you can organize technical sharing sessions, knowledge competitions, and other activities to share experience in using these platforms and make progress together, so that AI testing products can maximize their value in their work and help testers transform from traditional testing modes to efficient and intelligent testing modes.