Recently, LigaAI and Jihu GitLab announced a cooperation. The two parties will work together to explore a new paradigm of R&D efficiency in the AI era, provide AI-enabled one-stop R&D efficiency solutions , and make AI a new productivity for the development of Chinese programmers and enterprises.
Software research and development is a complex project involving many people, processes, and systems. LigaAI and Jihu GitLab have been actively exploring safe and efficient delivery methods. LigaAI has been innovating in the field of "AI + R&D collaboration" for many years. As a new generation of intelligent R&D collaboration platform with artificial intelligence technology as its core, LigaAI provides many enterprises with one-stop demand management, intelligent project assistant, R&D insights and other products and services through AI and powerful multi-terminal connection capabilities . Committed to simplifying complexity through AI scenarios, improving collaboration efficiency, and empowering R&D teams, LigaAI adheres to the concept of being smarter, lighter, and more efficient to help R&D teams innovate and grow with high quality and efficiency.
After years of intensive development, Jihu GitLab has developed into an enterprise-level integrated DevSecOps platform that includes agile project management, source code hosting, CI/CD, security compliance and many other functions. By improving R&D, products, operation and maintenance, testing, security Collaboration among other personnel, simplifying the operation and maintenance of complex tool chains, and accelerating the flow of value streams to improve software delivery efficiency while ensuring the safety and reliability of software development.
The cooperation between LigaAI and Jihu GitLab will build a new paradigm for R&D efficiency improvement in the AI era.
Paradigm 1: Intelligent management of R&D needs, new insights into R&D efficiency
During the requirements development process, multiple roles such as product, design, development, testing, and operation and maintenance need to work together, and seamless incremental transfer among cross-functional teams is the key to improving R&D efficiency. In traditional requirements management methods, requirements document writing, status updating and synchronization, and project progress tracking often result in a waste of human efficiency. In LigaAI, product managers can use AI to automatically write PRD documents and add context , let AI analyze the quality of requirements , and create to-do requirements with one click , quickly realizing two-way linkage between documents and requirements, which not only improves document quality and entry efficiency , and also ensure that every requirement is fully tracked and recorded.
In terms of demand management, LigaAI also provides advanced capabilities for team efficiency improvement, such as intelligent collection of personal to-dos , automatic generation of daily/weekly reports , visualization of project progress and risks , etc. With the help of AI capabilities, R&D personnel can quickly obtain important information such as to-dos, progress, obstacles, and risks from massive demand data, thereby improving decision-making efficiency and quality. LigaAI realizes the integrated collaboration of industry and research business, provides a new perspective for R&D, and truly focuses on delivering value.
Efficient requirements management is the beginning of rapid software delivery. LigaAI makes demand management simple, smart, and efficient, and also allows R&D personnel to have a more thorough understanding of demand. Coupled with Jihu's source code hosting and GitLab CI/CD , software development can be implemented quickly and with high quality delivery.
Paradigm 2: Human-machine paired intelligent programming, a new experience in code delivery
Coding, testing, reviewing and merging into the main branch is a common code delivery process for developers. AI can be integrated into every aspect of this process, such as using AI to assist in code writing , using AI to automatically generate test cases to test the code, AI to automatically recommend "hard-core" auditors to review the code, etc., which are advocated in XP Pair programming, with the help of AI, can be truly put into practice. This process is self-evident for improving efficiency. In addition, AI can also explain code blocks to help new employees or code reviewers quickly understand the logic behind the code. AI is transformed into a "personal assistant" for R&D personnel, allowing them to travel with AI and have unlimited coding power.
JiHu GitLab itself has completed JiHu Flow to standardize the software development process, improve code quality, and at the same time enhance the collaboration and R&D experience of large-scale teams. With the support of AI, the efficiency improvement brought by this workflow will be further amplified. The value will also be more prominent. Jihu GitLab will use AI to empower software development workflow and bring a new software delivery experience.
Simplifying code submission operations is also an important part of optimizing user experience. In the past, after developers completed coding, they had to switch to other tools to update the task status, which would cause problems with context switching and untimely information synchronization. LigaAI associates requirements and coding work through plug-ins to achieve unified management. Now, developers can directly view personal to-do and demand details , synchronize project status , and use the submission message function to automatically submit code information in the IDE without jumping back and forth, focusing on coding creation.
Paradigm 3: Intelligent resolution of vulnerability risks, new protection for security compliance
Security is the bottom line of software delivery. As the number of lines of software code and functions increase, security risks also increase. In the era of agile software delivery, security needs to be intervened in advance to ensure the security of software delivery. This has also been a hot topic in recent years. The origin of DevSecOps. GitLab has developed a large number of functions in DevSecOps. It not only introduces a large number of security testing methods (7 major security testing methods) , but also seamlessly integrates these security methods with the built-in CI/CD to realize automatic scanning of changed codes , R&D Personnel or code reviewers can see the scanned security vulnerabilities in MR and quickly repair them according to the repair suggestions, thus shortening the time to repair the vulnerabilities and improving the security of the changed code.
However, in this case, the interpretation of the security report still requires professional security knowledge or professional security personnel, and the discovery of vulnerabilities is mostly in the testing stage. With the support of AI, the discovery of security vulnerabilities will go further - when developers are coding , AI can identify potential security risks in the code and give modification suggestions; for security vulnerabilities discovered during the testing phase, AI Be able to interpret vulnerabilities like professional security professionals in words that developers can understand, and give repair suggestions. In this way, developers can solve the vulnerabilities in the code by themselves without the help of other personnel, and the entire security vulnerability repair cycle It will be greatly shortened, and the security of the code will be guaranteed.
In addition, project-level security and risks cannot be ignored. LigaAI integrates a large number of industry best practices, parses out many core R&D management indicators covering the three dimensions of team, project and engineering from massive native data , dynamically tracks software delivery performance throughout the entire process, and uses AI diagnosis to ensure the safety of project delivery.
During the project process, product managers can understand the health and potential risks of the team and project based on quantitative indicator data and AI-based intelligent suggestions, and quickly adjust strategic directions; technical leaders can gain insight into the coding performance and tasks of the R&D team from an engineering perspective. For circulation data and branch processing efficiency, with the help of diagnostic suggestions provided by AI intelligent experts, engineering bottlenecks can be identified in advance and accurately optimized; the CTO can quickly analyze the organization's R&D efficiency and key obstacles based on the "performance level benchmark", and AI can provide feasible solutions based on the actual situation. optimization suggestions to help the project be successfully completed.
write at the end
The field of software research and development has entered the AI era. Simply using manpower or traditional tools to improve research and development efficiency may be a thing with little effect. Learning to use the power of AI to amplify the energy of traditional tools and improve research and development efficiency is what everyone needs in the AI era. Something every software person needs to do. LigaAI and Jihu GitLab have already done a lot of exploration on this road. I believe that the combination of the two products can bring a new productivity tool to Chinese programmers and enterprises - an AI-empowered enterprise-level software development platform.
About LigaAI
LigaAI is a new generation of intelligent R&D collaboration platform. With artificial intelligence technology as our core, we are committed to simplifying complexity through AI scenarios, improving collaboration efficiency, and empowering our R&D teams. Starting from the specific work scenarios of developers, LigaAI uses artificial intelligence to abstract developers from complex chores and provide them with a simple and intelligent collaboration experience. It also provides digital, personalized and intelligent solutions for different types of organizations. Project collaboration platform.
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