Create Cortex/engineer/autonomouss_engineer.py

Cortex/engineer/autonomouss_engineer.py

@@ -0,0 +1,540 @@
+@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)