UnitGen 是一个用于生成微调代码的数据框架 —— 直接从你的代码库中生成微调数据：代码补全、测试生成、文档生成等。

Docs: https://gen.unitmesh.cc/

Thanks to OpenBayes for providing computing resources.

Finetune Model Examples:

Language support by Chapi

• supported:
  • Java
  • Kotlin
• doing:
  • TypeScript/JavaScript
  • Rust
• future:
  • Go
  • Python
  • C/C++
  • C#
  • Scala

Features:

• Code context strategy: Related code completion, Similar Code Completion
• Instruction Builder type: inline, block, after block, documentation, test gen
• Code quality filter and pipeline. Code smell, test smell, estimation and more.

Layered Architecture

Workflow

• Unique prompt. Integrated use of fine-tuning, evaluation, and tooling.
• Code quality pipeline. With estimate with code complex, bad smell, test bad smell, and more rules.
• Extendable customize quality thresholds. Custom rules, custom thresholds, custom quality type or more.

Keep the same prompt: AutoDev <-> UnitGen <-> UnitEval

AutoDev prompt template example:

Write unit test for following code. ${context.coc} ${context.framework} ${context.related_model} ```${context.language} ${context.selection} ``` 

Unit Picker prompt should keep the same structure as the AutoDev prompt. Prompt example:

Instruction( instruction = "Complete ${it.language} code, return rest code, no explaining", output = it.output, input = """  |```${it.language}  |${it.relatedCode}  |```  |  |Code:  |```${it.language}  |${it.beforeCursor}  |```""".trimMargin() )

UnitGen prompt should keep the same structure as the AutoDev prompt. Prompt example:

Complete ${language} code, return rest code, no explaining ```${language} ${relatedCode} ``` Code: ```${language} ${beforeCursor} ``` 

Optional quality type:

enum class CodeQualityType { BadSmell, TestBadSmell, JavaController, JavaRepository, JavaService, }

Custom thresholds' config:

data class BsThresholds( val bsLongParasLength: Int = 5, val bsIfSwitchLength: Int = 8, val bsLargeLength: Int = 20, val bsMethodLength: Int = 30, val bsIfLinesLength: Int = 3, )

Custom rules:

val apis = apiAnalyser.toContainerServices() val ruleset = RuleSet( RuleType.SQL_SMELL, "normal", UnknownColumnSizeRule(), LimitTableNameLengthRule() // more rules ) val issues = WebApiRuleVisitor(apis).visitor(listOf(ruleset)) // if issues are not empty, then the code has bad smell

for examples, see: examples folder

see in config-examples

download the latest version from GitHub Release

1. config project by processor.yml
2. run picker: java -jar unit-gen.jar

see in config-example

1.add dependency

dependencies { implementation("cc.unitmesh:unit-picker:0.1.5") implementation("cc.unitmesh:code-quality:0.1.5") }

2.config the unit-gen.yml file and connection.yml

3.write code

public class App { public static void main(String[] args) { List<InstructionType> builderTypes = new ArrayList<>(); builderTypes.add(InstructionType.RELATED_CODE_COMPLETION); List<CodeQualityType> codeQualityTypes = new ArrayList<>(); codeQualityTypes.add(CodeQualityType.BadSmell); codeQualityTypes.add(CodeQualityType.JavaService); PickerOption pickerOption = new PickerOption( "https://github.com/unit-mesh/unit-gen-testing", "master", "java", ".", builderTypes, codeQualityTypes, new BuilderConfig() ); SimpleCodePicker simpleCodePicker = new SimpleCodePicker(pickerOption); List<Instruction> output = simpleCodePicker.blockingExecute(); // handle output in here } } 

• abstract syntax tree: Chapi. Used features: multiple language to same data structure.
• legacy system analysis: Coca. Inspired: Bad Smell, Test Bad Smell
• architecture governance tool: ArchGuard. Used features: Estimation, Rule Lint (API, SQL)
• code database CodeDB. Used features: Code analysis pipeline

This code is distributed under the MPL 2.0 license. See LICENSE in this directory.