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In the development field of HarmonyOS Next, the intelligence of IDE plays a crucial role in the efficiency of developers.The AI empowerment integrated into the Cangjie language development process has brought developers an unprecedented programming experience.As a technician who is deeply involved in the development of related projects, I will explain in detail how AI plays a role in IDE and promotes the revolutionary improvement of development efficiency based on actual development scenarios.
1. AI-assisted programming scenarios
(I) Single-line completion vs. context-aware generation (interaction flowchart)
During the development of Cangjie language, AI-assisted programming functions are mainly reflected in single-line code generation and context-aware code snippet generation.
The single-line code generation function greatly improves the speed of code input.When the developer enters "(a:", the IDE will automatically trigger code generation and press the tab key to complete the code. This function is based on AI's learning of Cangjie language syntax and common code patterns, and can accurately predict the developer's intentions.
Context-aware code generation is smarter.It not only depends on the code currently entered, but also comprehensively considers the previous code structure, variable definition and other information.For example, after defining multiple variables, when the developer presses the Enter key, the code generation model will generate subsequent code snippets at the current position based on the types of these variables and the previous logic, and display them in grayscale. The developer can cancel it through the tab key completion or the Esc key.
To more clearly demonstrate the interaction process of these two functions, here is a simple interaction flowchart:
graph TD;
A[Start enter code] --> B{Whether to trigger a single line completion rule (such as input "(a:")};
B --
--> C[Show single line completion suggestions];
C --> D[Press the tab key to complete the code or continue to enter];
B -- No --> E[Enter end (such as press Enter)];
E --> F[Analyze context information];
F --> G [Generate context-aware code snippet (grayscale display)];
G --> H [press the tab key to complete the code or Esc key to cancel];
As can be seen from the flow chart, AI-assisted programming can provide timely and effective help in different code input stages, making the developer's programming process smoother.
2. Code generation practice
(I) Generate complete Agent DSL code from comments (sample demonstration)
With the help of the AI empowerment of IDE, it is possible to generate complete Agent DSL code from comments, which greatly accelerates the development process.Suppose we want to develop an intelligent task scheduling agent, we can first add the following comments to the code:
// @agent class, used for task scheduling
// @prompt[pattern=ScheduleTask] (
// action: "Schedule tasks based on task priority and resource conditions",
// purpose: "Improve task execution efficiency and ensure priority execution of important tasks",
// expectation: "Rational arrangement of task execution order and allocate resources"
// )
Based on these annotations, the IDE's AI function can automatically generate the Agent DSL code framework:
@agent class TaskScheduler {
@prompt[pattern=ScheduleTask] (
action: "Schedule tasks based on task priority and resource conditions",
purpose: "Improve task execution efficiency and ensure priority execution of important tasks",
expectation: "Reasonably arrange task execution order and allocate resources"
)
// Here the developers need to further implement the specific logic of task scheduling
func schedule(tasks: [Task], resources: [Resource]): [Task] {
// Code to be implemented
return [];
}
}
Developers only need to supplement specific business logic code based on the generated framework.This way of generating code from comments reduces a lot of duplicate code writing work, allowing developers to focus more on the implementation of core business logic.
3. Future prospects
(I) Possibility of deep integration between big models and IDE
With the continuous development of large-model technology, deep integration with IDE has great potential.In the future, IDEs may utilize the powerful semantic understanding and generation capabilities of large models to achieve smarter code prompts and error diagnosis.
For example, when a developer encounters a compilation error, the IDE can not only point out the error location, but also use the big model to analyze the cause of the error and provide detailed repair suggestions.During the code writing process, the big model can directly generate complete code modules based on the functional needs described by the developer, further improving development efficiency.
In addition, the big model can analyze the code structure of the entire project and provide optimization suggestions to help developers write more efficient and easier to maintain code.Although these functions are still in the conceivable stage, with the continuous advancement of technology, I believe that in the near future, the deep integration of large models and IDE will bring developers a more intelligent and efficient programming environment.
In the development of HarmonyOS Next, the AI empowerment of IDE has brought significant efficiency improvements to developers.From single-line code generation to context-aware code generation, to the deep integration of future big models and IDEs, AI has broad application prospects in the field of programming.As developers, we should actively embrace these changes, make full use of AI technology to improve development efficiency, and contribute more excellent applications to the development of the HarmonyOS Next ecosystem.
1. AI-assisted programming scenarios
(I) Single-line completion vs. context-aware generation (interaction flowchart)
During the development of Cangjie language, AI-assisted programming functions are mainly reflected in single-line code generation and context-aware code snippet generation.
The single-line code generation function greatly improves the speed of code input.When the developer enters "(a:", the IDE will automatically trigger code generation and press the tab key to complete the code. This function is based on AI's learning of Cangjie language syntax and common code patterns, and can accurately predict the developer's intentions.
Context-aware code generation is smarter.It not only depends on the code currently entered, but also comprehensively considers the previous code structure, variable definition and other information.For example, after defining multiple variables, when the developer presses the Enter key, the code generation model will generate subsequent code snippets at the current position based on the types of these variables and the previous logic, and display them in grayscale. The developer can cancel it through the tab key completion or the Esc key.
To more clearly demonstrate the interaction process of these two functions, here is a simple interaction flowchart:
graph TD;
A[Start enter code] --> B{Whether to trigger a single line completion rule (such as input "(a:")};
B --
--> C[Show single line completion suggestions];C --> D[Press the tab key to complete the code or continue to enter];
B -- No --> E[Enter end (such as press Enter)];
E --> F[Analyze context information];
F --> G [Generate context-aware code snippet (grayscale display)];
G --> H [press the tab key to complete the code or Esc key to cancel];
As can be seen from the flow chart, AI-assisted programming can provide timely and effective help in different code input stages, making the developer's programming process smoother.
2. Code generation practice
(I) Generate complete Agent DSL code from comments (sample demonstration)
With the help of the AI empowerment of IDE, it is possible to generate complete Agent DSL code from comments, which greatly accelerates the development process.Suppose we want to develop an intelligent task scheduling agent, we can first add the following comments to the code:
// @agent class, used for task scheduling
// @prompt[pattern=ScheduleTask] (
// action: "Schedule tasks based on task priority and resource conditions",
// purpose: "Improve task execution efficiency and ensure priority execution of important tasks",
// expectation: "Rational arrangement of task execution order and allocate resources"
// )
Based on these annotations, the IDE's AI function can automatically generate the Agent DSL code framework:
@agent class TaskScheduler {
@prompt[pattern=ScheduleTask] (
action: "Schedule tasks based on task priority and resource conditions",
purpose: "Improve task execution efficiency and ensure priority execution of important tasks",
expectation: "Reasonably arrange task execution order and allocate resources"
)
// Here the developers need to further implement the specific logic of task scheduling
func schedule(tasks: [Task], resources: [Resource]): [Task] {
// Code to be implemented
return [];
}
}
Developers only need to supplement specific business logic code based on the generated framework.This way of generating code from comments reduces a lot of duplicate code writing work, allowing developers to focus more on the implementation of core business logic.
3. Future prospects
(I) Possibility of deep integration between big models and IDE
With the continuous development of large-model technology, deep integration with IDE has great potential.In the future, IDEs may utilize the powerful semantic understanding and generation capabilities of large models to achieve smarter code prompts and error diagnosis.
For example, when a developer encounters a compilation error, the IDE can not only point out the error location, but also use the big model to analyze the cause of the error and provide detailed repair suggestions.During the code writing process, the big model can directly generate complete code modules based on the functional needs described by the developer, further improving development efficiency.
In addition, the big model can analyze the code structure of the entire project and provide optimization suggestions to help developers write more efficient and easier to maintain code.Although these functions are still in the conceivable stage, with the continuous advancement of technology, I believe that in the near future, the deep integration of large models and IDE will bring developers a more intelligent and efficient programming environment.
In the development of HarmonyOS Next, the AI empowerment of IDE has brought significant efficiency improvements to developers.From single-line code generation to context-aware code generation, to the deep integration of future big models and IDEs, AI has broad application prospects in the field of programming.As developers, we should actively embrace these changes, make full use of AI technology to improve development efficiency, and contribute more excellent applications to the development of the HarmonyOS Next ecosystem.