Introduction

Testing software, the cornerstone of producing application software products, plays an indispensable role in the software industry chain. However, due to various constraints such as technology and experience, China’s automated testing tools have long been monopolized by developed foreign countries, struggling in a “bottleneck” situation. Recently, we had the privilege of interviewing Xiang Shuangyun, a leading figure in the field of computer and network engineering R&D in China, to delve into his innovative work in AI-empowered industry automation testing.

Question 1:

In the current development status of automated testing tools both domestically and internationally, what opportunities and challenges do you think China faces in this field?

Xiang Shuangyun: Long-term constraints, including technological and experiential factors, have hindered China’s progress in the field of automated testing tools. However, with the advent of the Internet of Things, the 5G communication era, and the vast application scenarios in China, conditions have been created for the development of a new generation of domestic testing tools. The opportunity lies in our ability to surpass, utilize innovative research and development, and bridge the gap in this field. The challenge, however, is overcoming technological barriers to catch up with developed countries’ leading positions in automated testing tools.

Question 2:

Your innovative development of a real-time data mining system based on OLAP and data lake integration has made significant breakthroughs in the field of AI-empowered automated testing tools. Could you briefly introduce the core technology and features of this system?

Xiang Shuangyun: Our system leverages OLAP and data lake integration, utilizing an artificial intelligence engine to enhance UI unit test coverage. This means that typically, only 10% of manpower input is required to achieve over 90% test coverage. The system boasts high levels of automated coverage and the capability for continuous unmanned operation, significantly reducing human labor costs in the testing process. Additionally, the system incorporates extensive defect data standards and machine learning algorithms, establishing knowledge graphs and defect prediction models tailored to various business applications.

Question 3:

Your system has completed benchmark customer trials and extensive applications in industries such as telecommunications, power grids, finance, education, and smart cities. In these industries, what noticeable workforce reduction and efficiency improvement effects do you believe your system has brought to enterprises?

Xiang Shuangyun: Our system operates 24/7, increasing the automation rate of testing by around 80%, with human operations reduced to about 20%. In industries such as telecommunications, power grids, finance, education, and smart cities, enterprises utilizing our system have significantly saved operational costs and increased efficiency. This has also granted these enterprises a competitive advantage in the market, earning them high praise.

Question 4:

With the deep development and application of AI systems, blockchain, microservices, and big data technologies, do you foresee specialized testing in these fields posing new challenges for testing tools? What are your expectations for the future development of the AI-assisted testing field?

Xiang Shuangyun: As technology continues to advance, there will inevitably be an increased demand for specialized testing in these fields. I anticipate that in the future, the AI-assisted testing field will experience the development of a new ecosystem. We need to continually explore and leverage cutting-edge information technologies to meet the diverse needs of users in areas such as security, user experience, and operations monitoring. I believe that in the not-too-distant future, AI-assisted testing will contribute significantly to the intelligent transformation and development of Chinese enterprises.

Through this interview, we gained deeper insights into Xiang Shuangyun’s outstanding contributions to AI-empowered industry automation testing and his outlook for future development. His innovative research and the successful application of the system inject new vitality into China’s automated testing tool field and provide robust support for the industry’s intelligent transformation.