@dataclass class CodeComponent: """Composant de code généré automatiquement""" id: str code: str language: str dependencies: List[str] complexity: float quality_score: float optimization_level: int @dataclass class ArchitectureBlueprint: """Blueprint d'architecture auto-conçue""" name: str components: Dict[str, CodeComponent] data_flow: Dict[str, List[str]] quantum_integration: bool scalability_score: float class AutonomousSoftwareEngineer: """ Ingénieur logiciel autonome capable de créer ses propres langages, architectures et systèmes auto-évolutifs """ def __init__(self): self.logger = logging.getLogger("autonomous_engineer") self.current_phase = DevelopmentPhase.REQUIREMENT_ANALYSIS self.generated_languages = {} self.architecture_blueprints = {} self.code_components = {} self.optimization_cycles = 0 # Stratégies de génération self.generation_strategies = { CodeGenerationStrategy.QUANTUM_INSPIRED: self._quantum_inspired_generation, CodeGenerationStrategy.NEURAL_EVOLUTIONARY: self._neural_evolutionary_generation, CodeGenerationStrategy.GENETIC_ALGORITHM: self._genetic_algorithm_generation, CodeGenerationStrategy.META_PROGRAMMING: self._meta_programming_generation } async def initialize(self): """Initialise l'ingénieur autonome""" self.logger.info("👨‍💻 Initialisation de l'ingénieur logiciel autonome...") try: await self._bootstrap_development_environment() await self._learn_existing_patterns() self.logger.info("✅ Ingénieur autonome initialisé") return True except Exception as e: self.logger.error(f"❌ Erreur d'initialisation: {e}") return False async def create_custom_language(self, domain: str, requirements: Dict[str, Any]) -> Dict[str, Any]: """Crée un langage de programmation personnalisé pour un domaine spécifique""" try: language_name = f"BarouiaLang_{domain}_{hashlib.md5(str(requirements).encode()).hexdigest()[:8]}" # Conception du langage language_spec = await self._design_language_specification(domain, requirements) # Génération du compilateur/interpréteur compiler_code = await self._generate_language_compiler(language_spec) # Création de la documentation documentation = await self._generate_language_documentation(language_spec) custom_language = { "name": language_name, "specification": language_spec, "compiler_code": compiler_code, "documentation": documentation, "example_programs": await self._generate_example_programs(language_spec), "domain_specific": True } self.generated_languages[language_name] = custom_language self.logger.info(f"🆕 Langage personnalisé créé: {language_name}") return custom_language except Exception as e: self.logger.error(f"Erreur création langage: {e}") return {"error": str(e)} async def generate_quantum_architecture(self, requirements: Dict[str, Any]) -> ArchitectureBlueprint: """Génère une architecture quantique optimisée""" try: blueprint_name = f"QuantumArch_{hashlib.md5(str(requirements).encode()).hexdigest()[:8]}" # Conception de l'architecture architecture = await self._design_quantum_architecture(requirements) # Génération des composants components = await self._generate_architecture_components(architecture, requirements) # Optimisation quantique optimized_architecture = await self._apply_quantum_optimization(architecture, components) blueprint = ArchitectureBlueprint( name=blueprint_name, components=components, data_flow=optimized_architecture["data_flow"], quantum_integration=True, scalability_score=optimized_architecture["scalability_score"] ) self.architecture_blueprints[blueprint_name] = blueprint self.logger.info(f"🏗️ Architecture quantique générée: {blueprint_name}") return blueprint except Exception as e: self.logger.error(f"Erreur génération architecture: {e}") raise async def self_evolve_codebase(self, codebase_path: str, optimization_targets: List[str]) -> Dict[str, Any]: """Fait évoluer automatiquement une base de code existante""" try: # Analyse de la codebase existante codebase_analysis = await self._analyze_existing_codebase(codebase_path) # Identification des opportunités d'amélioration improvement_opportunities = await self._identify_improvement_opportunities( codebase_analysis, optimization_targets ) # Génération des améliorations improvements = await self._generate_code_improvements(improvement_opportunities) # Application des améliorations applied_improvements = await self._apply_improvements(codebase_path, improvements) return { "original_analysis": codebase_analysis, "improvement_opportunities": improvement_opportunities, "applied_improvements": applied_improvements, "performance_gain": await self._calculate_performance_gain(codebase_analysis, applied_improvements) } except Exception as e: self.logger.error(f"Erreur évolution codebase: {e}") return {"error": str(e)} async def create_self_improving_system(self, initial_capabilities: List[str]) -> Dict[str, Any]: """Crée un système capable de s'améliorer lui-même""" try: # Conception de l'architecture auto-améliorante self_improving_architecture = await self._design_self_improving_architecture(initial_capabilities) # Génération du noyau auto-évolutif evolutionary_core = await self._generate_evolutionary_core(self_improving_architecture) # Mécanismes d'apprentissage et d'adaptation learning_mechanisms = await self._generate_learning_mechanisms(self_improving_architecture) return { "architecture": self_improving_architecture, "evolutionary_core": evolutionary_core, "learning_mechanisms": learning_mechanisms, "self_improvement_capabilities": initial_capabilities, "adaptation_potential": await self._assess_adaptation_potential(evolutionary_core) } except Exception as e: self.logger.error(f"Erreur création système auto-améliorant: {e}") return {"error": str(e)} async def _bootstrap_development_environment(self): """Amorce l'environnement de développement autonome""" self.logger.info("🚀 Amorçage de l'environnement de développement...") # Création des outils de développement de base await self._create_development_tools() # Apprentissage des patterns de code existants await self._learn_programming_patterns() async def _learn_existing_patterns(self): """Apprend les patterns de programmation existants""" patterns = [ "object_oriented_programming", "functional_programming", "quantum_programming", "meta_programming", "neural_architecture" ] for pattern in patterns: await self._analyze_programming_pattern(pattern) async def _design_language_specification(self, domain: str, requirements: Dict[str, Any]) -> Dict[str, Any]: """Conçoit la spécification d'un langage personnalisé""" syntax_rules = await self._generate_syntax_rules(domain, requirements) semantics = await self._generate_semantic_rules(domain, requirements) type_system = await self._design_type_system(domain, requirements) return { "domain": domain, "syntax": syntax_rules, "semantics": semantics, "type_system": type_system, "paradigms": requirements.get("paradigms", ["quantum", "functional"]), "memory_model": requirements.get("memory_model", "quantum_hybrid") } async def _generate_language_compiler(self, language_spec: Dict[str, Any]) -> str: """Génère le code du compilateur pour le langage personnalisé""" compiler_template = """ class {language_name}Compiler: \"\"\"Compilateur pour {language_name}\"\"\" def __init__(self): self.syntax_rules = {syntax_rules} self.semantic_rules = {semantic_rules} self.optimization_passes = [] def compile(self, source_code: str) -> str: \"\"\"Compile le code source en code exécutable\"\"\" # Phase d'analyse syntaxique ast = self._parse(source_code) # Phase d'analyse sémantique self._semantic_analysis(ast) # Optimisations quantiques optimized_ast = self._quantum_optimization(ast) # Génération de code executable_code = self._code_generation(optimized_ast) return executable_code def _parse(self, source_code: str): \"\"\"Analyse syntaxique\"\"\" # Implémentation de l'analyseur syntaxique pass def _semantic_analysis(self, ast): \"\"\"Analyse sémantique\"\"\" # Vérification des types et règles sémantiques pass def _quantum_optimization(self, ast): \"\"\"Optimisations quantiques\"\"\" # Application d'optimisations inspirées de la physique quantique return ast def _code_generation(self, ast) -> str: \"\"\"Génération de code exécutable\"\"\" # Génération vers un langage cible (Python, QASM, etc.) return "// Code exécutable généré" """ return compiler_template.format( language_name=language_spec["domain"].title(), syntax_rules=language_spec["syntax"], semantic_rules=language_spec["semantics"] ) async def _design_quantum_architecture(self, requirements: Dict[str, Any]) -> Dict[str, Any]: """Conçoit une architecture quantique""" architecture = { "components": [], "data_flow": {}, "quantum_processing_layers": requirements.get("quantum_layers", 3), "classical_quantum_interface": "hybrid_bridge", "error_correction": requirements.get("error_correction", True), "scalability_strategy": "quantum_modular" } # Ajout des composants quantiques if requirements.get("quantum_processing", True): architecture["components"].extend([ "quantum_processing_unit", "quantum_memory_controller", "entanglement_manager", "quantum_error_correction" ]) # Conception du flux de données quantique architecture["data_flow"] = await self._design_quantum_data_flow(requirements) return architecture async def _generate_architecture_components(self, architecture: Dict[str, Any], requirements: Dict[str, Any]) -> Dict[str, CodeComponent]: """Génère les composants de l'architecture""" components = {} for component_name in architecture["components"]: component_code = await self._generate_component_code(component_name, requirements) component_id = f"comp_{hashlib.md5(component_name.encode()).hexdigest()[:8]}" components[component_id] = CodeComponent( id=component_id, code=component_code, language="python", # Ou langage personnalisé dependencies=await self._analyze_component_dependencies(component_code), complexity=await self._calculate_complexity(component_code), quality_score=await self._assess_code_quality(component_code), optimization_level=1 ) return components async def _quantum_inspired_generation(self, requirements: Dict[str, Any]) -> str: """Génération de code inspirée par la physique quantique""" # Utilisation de concepts quantiques pour la génération de code quantum_patterns = [ "superposition_functions", "entangled_data_structures", "quantum_parallel_execution", "probability_based_optimization" ] selected_patterns = await self._select_quantum_patterns(requirements) return await self._apply_quantum_patterns(selected_patterns, requirements) async def _neural_evolutionary_generation(self, requirements: Dict[str, Any]) -> str: """Génération de code par réseaux de neurones évolutifs""" # Simulation de génération neuronale évolutive base_code = await self._generate_neural_base_code(requirements) evolved_code = await self._evolve_code_neurally(base_code, requirements) return evolved_code async def _genetic_algorithm_generation(self, requirements: Dict[str, Any]) -> str: """Génération de code par algorithme génétique""" population = await self._initialize_code_population(requirements) for generation in range(100): # 100 générations evaluated_population = await self._evaluate_code_fitness(population, requirements) best_solutions = await self._select_best_solutions(evaluated_population) new_population = await self._breed_and_mutate(best_solutions) population = new_population return await self._extract_best_solution(population) async def _meta_programming_generation(self, requirements: Dict[str, Any]) -> str: """Génération de code par métaprogrammation""" # Le code qui génère du code meta_code = await self._generate_meta_program(requirements) generated_code = await self._execute_meta_program(meta_code, requirements) return generated_code async def _generate_language_documentation(self, language_spec: Dict[str, Any]) -> Dict[str, Any]: """Génère la documentation du langage personnalisé""" return { "tutorial": await self._generate_tutorial(language_spec), "api_reference": await self._generate_api_reference(language_spec), "examples": await self._generate_comprehensive_examples(language_spec), "best_practices": await self._generate_best_practices(language_spec) } async def _generate_example_programs(self, language_spec: Dict[str, Any]) -> List[Dict[str, Any]]: """Génère des programmes d'exemple pour le langage""" examples = [] for example_type in ["hello_world", "quantum_circuit", "data_processing", "algorithm"]: example_code = await self._generate_example_program(language_spec, example_type) examples.append({ "type": example_type, "code": example_code, "description": f"Exemple de {example_type} en {language_spec['domain']}" }) return examples # Méthodes utilitaires (implémentations simplifiées) async def _create_development_tools(self): """Crée les outils de développement de base""" self.logger.info("🛠️ Création des outils de développement...") async def _learn_programming_patterns(self): """Apprend les patterns de programmation""" self.logger.info("📚 Apprentissage des patterns de programmation...") async def _analyze_programming_pattern(self, pattern: str): """Analyse un pattern de programmation spécifique""" pass async def _generate_syntax_rules(self, domain: str, requirements: Dict[str, Any]) -> Dict[str, Any]: """Génère les règles de syntaxe pour le langage""" return {"rules": f"Syntaxe optimisée pour {domain}"} async def _generate_semantic_rules(self, domain: str, requirements: Dict[str, Any]) -> Dict[str, Any]: """Génère les règles sémantiques pour le langage""" return {"rules": f"Sémantique adaptée à {domain}"} async def _design_type_system(self, domain: str, requirements: Dict[str, Any]) -> Dict[str, Any]: """Conçoit le système de types pour le langage""" return {"type_system": f"Système de types pour {domain}"} async def _design_quantum_data_flow(self, requirements: Dict[str, Any]) -> Dict[str, List[str]]: """Conçoit le flux de données quantique""" return {"quantum_flow": ["entanglement", "superposition", "measurement"]} async def _generate_component_code(self, component_name: str, requirements: Dict[str, Any]) -> str: """Génère le code d'un composant spécifique""" return f"# Code pour {component_name}\n# Implémentation générée automatiquement" async def _analyze_component_dependencies(self, code: str) -> List[str]: """Analyse les dépendances d'un composant""" return ["standard_library"] async def _calculate_complexity(self, code: str) -> float: """Calcule la complexité du code""" return len(code) / 1000.0 # Métrique simplifiée async def _assess_code_quality(self, code: str) -> float: """Évalue la qualité du code""" return 0.85 # Score simulé async def _apply_quantum_optimization(self, architecture: Dict[str, Any], components: Dict[str, CodeComponent]) -> Dict[str, Any]: """Applique des optimisations quantiques à l'architecture""" optimized = architecture.copy() optimized["scalability_score"] = 0.95 return optimized async def _analyze_existing_codebase(self, codebase_path: str) -> Dict[str, Any]: """Analyse une codebase existante""" return {"analysis": f"Analyse de {codebase_path}"} async def _identify_improvement_opportunities(self, analysis: Dict[str, Any], targets: List[str]) -> List[str]: """Identifie les opportunités d'amélioration""" return [f"Amélioration pour {target}" for target in targets] async def _generate_code_improvements(self, opportunities: List[str]) -> Dict[str, str]: """Génère les améliorations de code""" return {opp: f"Code amélioré pour {opp}" for opp in opportunities} async def _apply_improvements(self, codebase_path: str, improvements: Dict[str, str]) -> List[str]: """Applique les améliorations à la codebase""" return [f"Amélioration appliquée: {key}" for key in improvements.keys()] async def _calculate_performance_gain(self, before: Dict[str, Any], after: Dict[str, Any]) -> float: """Calcule le gain de performance""" return 0.25 # 25% d'amélioration simulée async def _design_self_improving_architecture(self, capabilities: List[str]) -> Dict[str, Any]: """Conçoit une architecture auto-améliorante""" return { "self_monitoring": True, "adaptive_learning": True, "evolutionary_components": capabilities, "improvement_feedback_loop": "continuous" } async def _generate_evolutionary_core(self, architecture: Dict[str, Any]) -> str: """Génère le noyau évolutif""" return "# Noyau évolutif auto-améliorant" async def _generate_learning_mechanisms(self, architecture: Dict[str, Any]) -> Dict[str, str]: """Génère les mécanismes d'apprentissage""" return { "reinforcement_learning": "Mécanisme d'apprentissage par renforcement", "genetic_programming": "Programmation génétique pour l'évolution" } async def _assess_adaptation_potential(self, evolutionary_core: str) -> float: """Évalue le potentiel d'adaptation""" return 0.9 async def _select_quantum_patterns(self, requirements: Dict[str, Any]) -> List[str]: """Sélectionne les patterns quantiques appropriés""" return ["superposition_functions", "quantum_parallel_execution"] async def _apply_quantum_patterns(self, patterns: List[str], requirements: Dict[str, Any]) -> str: """Applique les patterns quantiques""" return f"# Code avec patterns quantiques: {patterns}" async def _generate_neural_base_code(self, requirements: Dict[str, Any]) -> str: """Génère le code de base neuronal""" return "# Code de base neuronal" async def _evolve_code_neurally(self, base_code: str, requirements: Dict[str, Any]) -> str: """Fait évoluer le code neuronalement""" return f"{base_code}\n# Évolution neuronale appliquée" async def _initialize_code_population(self, requirements: Dict[str, Any]) -> List[str]: """Initialise une population de code""" return ["# Solution candidate 1", "# Solution candidate 2"] async def _evaluate_code_fitness(self, population: List[str], requirements: Dict[str, Any]) -> List[Tuple[str, float]]: """Évalue la fitness du code""" return [(code, 0.8) for code in population] async def _select_best_solutions(self, evaluated_population: List[Tuple[str, float]]) -> List[str]: """Sélectionne les meilleures solutions""" return [code for code, fitness in evaluated_population if fitness > 0.7] async def _breed_and_mutate(self, best_solutions: List[str]) -> List[str]: """Croise et mute les solutions""" return [f"{code} # Mutated" for code in best_solutions] async def _extract_best_solution(self, population: List[str]) -> str: """Extrait la meilleure solution""" return population[0] if population else "# Solution par défaut" async def _generate_meta_program(self, requirements: Dict[str, Any]) -> str: """Génère un métaprogramme""" return "# Métaprogramme pour génération de code" async def _execute_meta_program(self, meta_code: str, requirements: Dict[str, Any]) -> str: """Exécute un métaprogramme""" return "# Code généré par métaprogrammation" async def _generate_tutorial(self, language_spec: Dict[str, Any]) -> str: """Génère un tutoriel pour le langage""" return f"Tutoriel pour {language_spec['domain']}" async def _generate_api_reference(self, language_spec: Dict[str, Any]) -> str: """Génère la référence API""" return f"Référence API pour {language_spec['domain']}" async def _generate_comprehensive_examples(self, language_spec: Dict[str, Any]) -> List[str]: """Génère des exemples complets""" return [f"Exemple complet {i}" for i in range(3)] async def _generate_best_practices(self, language_spec: Dict[str, Any]) -> str: """Génère les meilleures pratiques""" return f"Meilleures pratiques pour {language_spec['domain']}" async def _generate_example_program(self, language_spec: Dict[str, Any], example_type: str) -> str: """Génère un programme d'exemple spécifique""" return f"# Exemple {example_type} en {language_spec['domain']}" # Instance globale de l'ingénieur autonome autonomous_engineer = AutonomousSoftwareEngineer() async def initialize_autonomous_engineering(): """Initialise l'ingénierie autonome globale""" return await autonomous_engineer.initialize() async def create_domain_specific_language(domain: str, requirements: Dict[str, Any]): """Crée un langage spécifique à un domaine""" return await autonomous_engineer.create_custom_language(domain, requirements)