RFC GO-ASL-0001

Goal-Oriented Application Specification Language (GO-ASL)

Title: Goal-Oriented Application Specification Language (GO-ASL) Status: Draft Author: Eric Andre (jonescomas@gmx.de) (concept proposal) Date: 2025-07-11

Table of Contents

1. Abstract

2. Introduction

3. Motivation

4. Design Goals

5. Syntax Overview

6. Section Definitions

   • goal

   • constraints

   • preferences

   • principles

   • non_functional_requirements

   • observability

   • reasoning

   • dependencies

   • code_quality

   • internationalization

   • environments

   • data_privacy

   • documentation

7. Usage Flow with LLMs

8. Examples

9. Security Considerations

10. Future Work

11. References

12. JSON Schema

1. Abstract

The Goal-Oriented Application Specification Language (GO-ASL) defines a structured, machine-readable DSL for instructing large language models (LLMs) to produce application code, configurations, and deployment artifacts. Unlike specification-heavy DSLs that require detailed architectural inputs, GO-ASL focuses on expressing high-level goals, constraints, and principles, allowing LLMs to act as intelligent software architects and generate complete applications end-to-end.

2. Introduction

Large Language Models (LLMs) are increasingly capable of generating production-quality software systems. However, unstructured natural language prompts are often ambiguous, leading to misinterpretations, missing requirements, or architectures unsuitable for real-world deployment.

GO-ASL provides a standardized format to capture:

• the purpose of the application

• required and prohibited technologies

• non-functional requirements (NFRs)

• architectural and design principles

• constraints on deployment, security, and compliance

The specification ensures consistent communication between developers and LLMs, enabling reproducibility and higher confidence in generated software artifacts.

3. Motivation

Several challenges exist with LLM-based software generation:

• Developers often omit critical NFRs like security, performance, or compliance.

• LLMs generate divergent outputs due to prompt ambiguities.

• Professional teams require control over coding standards, architecture patterns, and dependencies.

• Industry-grade systems must comply with privacy and regulatory requirements.

• Observability and error handling are frequently neglected in AI-generated code.

GO-ASL aims to bridge this gap by acting as a contract between the developer and the LLM, clarifying both what should be built and how it should adhere to software engineering best practices.

4. Design Goals

GO-ASL was designed to:

• Be goal-centric, not architecture-centric.

• Support minimal inputs for rapid prototyping.

• Allow precise constraints for enterprise-grade systems.

• Be human-readable (YAML syntax).

• Provide sufficient information for LLMs to reason and plan before generating code.

• Support iterative conversations and refinements between human developers and LLMs.

5. Syntax Overview

A GO-ASL document is written in YAML and consists of optional and required sections.

Top-level format:

goal:
  ...

constraints:
  ...

preferences:
  ...

principles:
  ...

non_functional_requirements:
  ...

observability:
  ...

reasoning:
  ...

dependencies:
  ...

code_quality:
  ...

internationalization:
  ...

environments:
  ...

data_privacy:
  ...

documentation:
  ...

Except for goal, all sections are optional.

6. Section Definitions

6.1 goal

Required. Defines the purpose and high-level functionality of the desired application.

Fields:

• name: Short name of the application.

• description: Natural language explanation of core functionality.

Example:

goal:
  name: TaskMaster API
  description: >
    A RESTful API for managing personal tasks. Users can register,
    log in, and create, read, update, or delete tasks. Each task
    has a title, description, due date, and completion status.

6.2 constraints

Specifies non-negotiable requirements.

• must_use_technologies: List of required technologies.

• must_not_use_technologies: List of forbidden technologies.

• deployment_target: E.g. docker-compose, kubernetes, serverless.

• scale: low | medium | high.

• budget: low | medium | high.

• licensing: open-source | proprietary.

Example:

constraints:
  must_use_technologies:
    - Java
  must_not_use_technologies:
    - JavaScript
  deployment_target: docker-compose
  scale: low
  budget: low
  licensing: open-source

6.3 preferences

Soft preferences, not hard constraints.

• preferred_languages

• preferred_ui_frameworks

• preferred_database

• style_tone: concise | verbose | well-commented

Example:

preferences:
  preferred_languages:
    - Java
  preferred_database:
    - PostgreSQL
  style_tone: well-commented

6.4 principles

Controls architectural and coding philosophy.

• architecture_patterns: e.g. Clean Architecture, Layered Architecture.

• design_principles: SOLID, DRY, KISS.

• coding_standards: Style guides or conventions.

• documentation.style: brief | detailed | none.

• testing_strategy: TDD, BDD, etc.

Example:

principles:
  architecture_patterns:
    - Clean Architecture
  design_principles:
    - SOLID
  coding_standards:
    - Google Java Style
  documentation:
    style: detailed
  testing_strategy:
    - TDD

6.5 non_functional_requirements

Specifies NFRs such as:

• performance:

  • latency_ms

  • throughput_rps

• scalability:

  • horizontal_scaling

• security:

  • auth_mechanisms

  • data_encryption

• compliance:

  • GDPR, HIPAA, PCI DSS

• maintainability:

  • max complexity

  • max file length

Example:

non_functional_requirements:
  performance:
    latency_ms: 200
    throughput_rps: 100
  security:
    auth_mechanisms:
      - JWT
    data_encryption: at-rest

6.6 observability

Specifies monitoring and error handling practices.

• logging:

  • level

  • format

• metrics:

  • monitoring tools

• tracing:

  • enabled

  • tool

Example:

observability:
  logging:
    level: info
    format: json
  tracing:
    enabled: true
    tool: OpenTelemetry

6.7 reasoning

Controls whether the LLM should:

• produce an architectural plan first

• explain decisions

• pause for human validation

Example:

reasoning:
  planning_mode: true
  explain_design_choices: true
  require_user_approval_before_coding: true

6.8 dependencies

Lists allowed or forbidden libraries.

• approved_libraries

• forbidden_libraries

• min_version_requirements

Example:

dependencies:
  approved_libraries:
    - spring-boot
  forbidden_libraries:
    - log4j
  min_version_requirements:
    spring-boot: "3.1.0"

6.9 code_quality

Specifies quality targets.

• test_coverage_minimum

• static_analysis:

  • enabled

  • tools

• enforce_linting

Example:

code_quality:
  test_coverage_minimum: 80
  static_analysis:
    enabled: true
    tools:
      - SonarQube
  enforce_linting: true

6.10 internationalization

Indicates multi-language support.

• supported_languages

• default_language

Example:

internationalization:
  supported_languages:
    - en
    - de
  default_language: en

6.11 environments

Defines environment-specific differences.

Example:

environments:
  dev:
    logging_level: debug
  prod:
    logging_level: info
    secrets_management: vault

6.12 data_privacy

Describes privacy and compliance practices.

• pii_handling:

  • anonymize_data

  • logging_of_pii

• audit_trails

Example:

data_privacy:
  pii_handling:
    anonymize_data: true
    logging_of_pii: false
  audit_trails: true

6.13 documentation

Specifies formats and level of detail.

• formats: markdown, OpenAPI, etc.

• include_architecture_diagrams: true | false

Example:

documentation:
  formats:
    - markdown
    - OpenAPI 3.0
  include_architecture_diagrams: true

7. Usage Flow with LLMs

A typical workflow:

1. Developer writes a GO-ASL spec.

2. LLM reads the spec.

3. LLM:

   • Proposes architecture.

   • Explains decisions (if reasoning enabled).

4. Developer approves or modifies the plan.

5. LLM generates:

   • source code

   • configurations

   • deployment artifacts

6. Developer iteratively refines outputs.

8. Examples

Minimal GO-ASL

goal:
  name: Blogify
  description: >
    A simple blog platform where users can post articles and comment.

Full GO-ASL Example

goal:
  name: TaskExecutorPro
  description: >
    A scalable Java Spring Boot 3.4 application for executing background tasks.
    The system should support task scheduling, monitoring, retries for failed
    tasks, and allow users to cancel running tasks. All task states should be
    persisted in a database with regular backups. The system should expose
    metrics and logs for monitoring purposes.

constraints:
  must_use_technologies:
    - Java
    - Spring Boot 3.4
  deployment_target: kubernetes
  scale: high
  budget: medium
  licensing: open-source

preferences:
  preferred_database:
    - PostgreSQL
  style_tone: well-commented

principles:
  architecture_patterns:
    - Clean Architecture
  design_principles:
    - SOLID
    - DRY
  coding_standards:
    - Google Java Style
  documentation:
    style: detailed
  testing_strategy:
    - TDD

non_functional_requirements:
  performance:
    latency_ms: 100
    throughput_rps: 1000
  scalability:
    horizontal_scaling: true
  security:
    auth_mechanisms:
      - OAuth2
    data_encryption: at-rest
  maintainability:
    max_cyclomatic_complexity: 10

observability:
  logging:
    level: info
    format: json
  metrics:
    tool: Prometheus
  tracing:
    enabled: true
    tool: OpenTelemetry

reasoning:
  planning_mode: true
  explain_design_choices: true
  require_user_approval_before_coding: true

dependencies:
  approved_libraries:
    - spring-boot
    - spring-data-jpa
    - spring-security
    - spring-actuator
    - spring-retry
  forbidden_libraries:
    - log4j
  min_version_requirements:
    spring-boot: "3.4.0"

code_quality:
  test_coverage_minimum: 85
  static_analysis:
    enabled: true
    tools:
      - SonarQube
  enforce_linting: true

internationalization:
  supported_languages:
    - en

environments:
  dev:
    logging_level: debug
  prod:
    logging_level: info
    secrets_management: vault

data_privacy:
  pii_handling:
    anonymize_data: true
    logging_of_pii: false
  audit_trails: true

documentation:
  formats:
    - markdown
    - OpenAPI 3.0
  include_architecture_diagrams: true

9. Security Considerations

GO-ASL itself defines no executable behavior and thus poses no direct security risks. However:

• LLMs interpreting GO-ASL must sanitize outputs to avoid injection vulnerabilities.

• Sensitive data in specs (e.g. database URLs) should not be committed to public repositories.

10. Future Work

Potential future improvements:

• Formal schemas for validating GO-ASL documents.

• Standard vocabularies for common app goals.

• Integration with IDE plugins for GO-ASL editing.

• Tooling for converting GO-ASL to model-specific prompts automatically.

• Multi-model compatibility guidelines.

11. JSON Schema

JSON Schema GO-ASL 1.0.0