Performance insights help enterprises identify bottlenecks, optimize resources, improve collaboration and quality through transparent quantified data presentation and AI intelligent coding tools. This enables data-driven decision making and improves development efficiency.
Significance of performance insights
Performance insights provide transparent quantified data presentation, such as development cycle time, code submission frequency, error rate, and resource utilization metrics, enabling enterprises to:
Identify bottlenecks: Through data analysis, enterprises can quickly identify inefficient links or bottlenecks in the development process and take targeted measures for optimization.
Optimize resource allocation: Understanding resource usage helps enterprises reasonably allocate human and technical resources, avoid waste, and improve resource utilization efficiency.
Improve team collaboration: By gaining insights into team work patterns and efficiency, collaboration and communication between team members are promoted, improving overall work efficiency.
Quality improvement: By monitoring error rates and code quality metrics, enterprises can identify problems in a timely manner and make improvements, enhancing product quality.
Data-driven decision making: By making decisions based on data, enterprises can develop strategies and plans more scientifically, reducing the blindness and risks in decision making.
Measure the benefits of AI coding assistant
In the AIGC (artificial intelligence generated content) era, large models have had a significant impact on the software programming field. AI intelligent coding tools such as Lingma, through assisted programming, not only improve development efficiency but also enhance code quality and developer experience.
Improve coding efficiency: AI tools automatically generate code snippets and provide code suggestions, reducing manual coding workload and significantly accelerating coding speed.
Reduce repetitive work: By automatically generating repetitive code, developers can focus on more creative and complex tasks.
Improve code quality: Generate code according to best practices and enterprise code standards, reduce human errors, and enhance code consistency and reliability.
Enhance developer experience: Provide instant code suggestions and error prompts, helping developers solve problems faster and maintain flow state.
Quantify efficiency improvement: Through data analysis, the proportion of AI-generated code is high, development speed and code quality are significantly improved, and defect density is reduced.
For more information about the specific impact brought by AI usage, see Measure the benefits of AI Coding Assistant.
AsiaInfo: AsiaInfo's journey of intelligent R&D practices
AsiaInfo has promoted intelligent R&D practices by introducing Alibaba Cloud Lingma, significantly improving code development efficiency and quality. They have explored a full-process intelligent tool set to enhance overall software development efficiency. For more information, see AsiaInfo: AsiaInfo's journey of intelligent R&D practices.