Building a Simple Java Framework with ChatGPT: From Concept to Performance Testing

The article explains how to create a simple Java framework with the assistance of ChatGPT, starting from identifying the problem of manually mapping enum descriptions to the front‑end in a back‑end‑front‑end separated architecture.

It proposes an annotation‑based solution where an @EnumInfo annotation marks enum classes with fields name, key, and desc, enabling automatic generation of a key‑to‑description map.

Key code snippets include an initialization method for the enum map:

public HashMap<String, Map<Integer, String>> initEnumMap() {
    enumMap = new HashMap<>();
    enumMap.put("前端获取枚举map的key", XXXEnum.getEnumMap());
    // ... more entries
    return enumMap;
}

A REST endpoint that returns the aggregated enum map is shown:

@RequestMapping("/getEnum")
public Result<Map<String, Map<String, String>>> getEnum(String enumKey) {
    try {
        EnumContext enumContext = EnumContextFactory.getEnumContext();
        newEnumMap = enumContext.getEnumIndexMap();
        // additional manual entries if needed
    } catch (Exception e) {
        log.error("获取枚举map出错！enumKey:{}", enumKey, e);
        return Result.createFail(e.getMessage());
    }
    if (StringUtils.isBlank(enumKey)) {
        return Result.createWithSuc(newEnumMap);
    }
    Map<String, Map<String, String>> resultMap = new HashMap<>();
    resultMap.put(enumKey, newEnumMap.get(enumKey));
    return Result.createWithSuc(resultMap);
}

The article lists the core classes generated with ChatGPT, such as PackageScanner, PropertiesUtils, EnumInfo, EnumContext, EnumContextFactory, EnumDefinition, EnumDefinitionRegistry, and DefaultEnumFactory, illustrating their responsibilities.

An example of the @EnumInfo annotation and a sample enum is provided:

@EnumInfo(name = "StatusEnum", key = "code", desc = "description")
public enum StatusEnum {
    SUCCESS(200, "Success"),
    ERROR(500, "Error");
    private final int code;
    private final String description;
    StatusEnum(int code, String description) {
        this.code = code;
        this.description = description;
    }
    public int getCode() { return code; }
    public String getDescription() { return description; }
}

After the framework is built, the article advises performing performance testing using tools like LoadRunner or Apache JMeter, reporting that on a 4C4G machine the service can sustain 1000 QPS with about 30 % CPU usage and stable memory consumption.

In conclusion, the guide shows that ChatGPT can effectively assist developers in rapidly prototyping a Java backend framework, reducing manual coding effort, and delivering a maintainable solution for enum mapping in front‑end integration scenarios.