Enhance AI fix suggestion mechanism with LLM validation and source code context

Author: Dushxn

src/main/java/org/springforge/qualityassurance/actions/AnalyzeQualityAction.kt

@@ -80,13 +80,18 @@ class AnalyzeQualityAction : AnAction("Analyze Code Quality") {
                     println("🟩 Initial report shown — overall: ${analysisResult.overall_display}, violations: ${analysisResult.total_violations}")
 
                     // ── STEP 5: Call Gemini for AI fix suggestions ────────────
+                    // If LLM validation was performed, fix_suggestions are already inline —
+                    // skip the separate /generate-fixes call.
                     var aiFixCount = 0
-                    if (analysisResult.anti_patterns.isNotEmpty()) {
+                    if (analysisResult.anti_patterns.isNotEmpty() && !analysisResult.llm_enhanced) {
                         indicator.text     = "🤖 Generating AI fix suggestions (Gemini)..."
                         indicator.fraction = 0.80
 
                         try {
-                            val fixResult = MLServiceClient.generateProjectFixes(analysisResult)
+                            val sourceMap = fileFeatures.associate { it.file_name to (it.source_code ?: "") }
+                                .filterValues { it.isNotBlank() }
+                                .ifEmpty { null }
+                            val fixResult = MLServiceClient.generateProjectFixes(analysisResult, sourceMap)
                             aiFixCount = fixResult.total_fixes
                             indicator.fraction = 0.95
 
@@ -107,6 +112,9 @@ class AnalyzeQualityAction : AnAction("Analyze Code Quality") {
                                 )
                             }
                         }
+                    } else if (analysisResult.llm_enhanced) {
+                        aiFixCount = analysisResult.fix_suggestions.size
+                        println("🟩 LLM-enhanced: ${analysisResult.fix_suggestions.size} inline fixes, ${analysisResult.false_positives_filtered} false positives filtered")
                     }
 
                     indicator.fraction = 1.0

src/main/java/org/springforge/qualityassurance/analysis/PsiFeatureExtractor.kt

@@ -435,7 +435,8 @@ object PsiFeatureExtractor {
             has_transaction         = hasTransaction,
             violates_layer_separation = violatesLayer,
             uses_new_keyword        = usesNewKeyword,
-            has_broad_catch         = hasBroadCatch
+            has_broad_catch         = hasBroadCatch,
+            source_code             = psiFile.text
         )
     }
 

src/main/java/org/springforge/qualityassurance/model/AntiPatternDetail.kt

@@ -8,5 +8,9 @@ data class AntiPatternDetail(
     val confidence: Double,
     val files: List<String>,
     val description: String,
-    val recommendation: String
+    val recommendation: String,
+    // v3: LLM validation fields
+    val llm_validated: Boolean = false,
+    val llm_description: String = "",
+    val fix_suggestion: Map<String, Any>? = null
 )

src/main/java/org/springforge/qualityassurance/model/CombinedAnalysisResult.kt

@@ -17,5 +17,9 @@ data class CombinedAnalysisResult(
     val files_with_violations: Int = 0,
     val projected_score_after_fixes: Double = 0.0,
     val quality_summary: String = "",
-    val violation_summary: String = ""
+    val violation_summary: String = "",
+    // v3: LLM validation summary
+    val llm_enhanced: Boolean = false,
+    val false_positives_filtered: Int = 0,
+    val fix_suggestions: List<FixSuggestion> = emptyList()
 )

src/main/java/org/springforge/qualityassurance/model/FileFeatureModel.kt

@@ -50,5 +50,10 @@ data class FileFeatureModel(
 
     // v2: new anti-pattern detection flags
     var uses_new_keyword        : Boolean = false,  // tight_coupling_new_keyword
-    var has_broad_catch         : Boolean = false   // broad_catch
+    var has_broad_catch         : Boolean = false,  // broad_catch
+
+    // v3: source code for LLM validation — when provided, Gemini validates
+    // ML predictions against real code, filters false positives, and generates
+    // context-aware fixes. When null, ML-only behavior is preserved.
+    var source_code             : String? = null
 )

src/main/java/org/springforge/qualityassurance/model/FixSuggestion.kt

@@ -40,5 +40,7 @@ data class SingleFixRequest(
 /** Request body for POST /generate-fixes (full project batch). */
 data class FixRequest(
     val anti_patterns        : List<AntiPatternDetail> = emptyList(),
-    val architecture_pattern : String                  = "layered"
+    val architecture_pattern : String                  = "layered",
+    // v3: optional source code map for context-aware fix generation
+    val file_sources         : Map<String, String>?    = null
 )

src/main/java/org/springforge/qualityassurance/network/MLServiceClient.kt

@@ -24,7 +24,7 @@ import java.util.concurrent.TimeUnit
  */
 object MLServiceClient {
 
-    private const val BASE_URL = "https://api.springforge.dev/quality"
+    private const val BASE_URL = "http://127.0.0.1:8000"
 
     private val client = OkHttpClient.Builder()
         .connectTimeout(30, TimeUnit.SECONDS)
@@ -64,11 +64,17 @@ object MLServiceClient {
     /**
      * Calls POST /generate-fixes to get batch AI fix suggestions for all
      * anti-patterns detected by [analyzeProjectFull].
+     *
+     * @param fileSources optional map of file_name → source_code for context-aware fixes
      */
-    fun generateProjectFixes(analysisResult: CombinedAnalysisResult): ProjectFixResult {
+    fun generateProjectFixes(
+        analysisResult: CombinedAnalysisResult,
+        fileSources: Map<String, String>? = null
+    ): ProjectFixResult {
         val request = FixRequest(
             anti_patterns        = analysisResult.anti_patterns,
-            architecture_pattern = normaliseArchitecture(analysisResult.architecture_pattern)
+            architecture_pattern = normaliseArchitecture(analysisResult.architecture_pattern),
+            file_sources         = fileSources
         )
         val json = JsonUtil.toJson(request)
         println("🤖 Calling Gemini for ${analysisResult.anti_patterns.size} fix suggestions…")

src/main/java/org/springforge/qualityassurance/toolwindow/QualityToolWindowPanel.kt

@@ -87,10 +87,14 @@ class QualityToolWindowPanel : JPanel() {
         sb.ln("Total Issues Found   : ${r.total_violations} violations across ${r.files_with_violations} files")
         when {
             fixes != null         -> sb.ln("AI Fix Suggestions   : ${fixes.total_fixes} (powered by Gemini 🤖)")
+            r.fix_suggestions.isNotEmpty() -> sb.ln("AI Fix Suggestions   : ${r.fix_suggestions.size} (LLM-validated, inline 🤖)")
             geminiWarning != null -> sb.ln("AI Fix Suggestions   : ⚠️  $geminiWarning")
             r.total_violations > 0 -> sb.ln("AI Fix Suggestions   : ⏳ Loading Gemini suggestions...")
             else                  -> sb.ln("AI Fix Suggestions   : N/A (no violations)")
         }
+        if (r.llm_enhanced) {
+            sb.ln("LLM Validation       : ✅ Gemini-validated (${r.false_positives_filtered} false positives filtered)")
+        }
         sb.ln()
 
         // ── QUALITY SCORE DASHBOARD ───────────────────────────────────────────
@@ -130,8 +134,10 @@ class QualityToolWindowPanel : JPanel() {
             sb.ln("   Your code follows ${r.architecture_pattern} best practices. 🎉")
         } else {
             // Build a lookup map: anti_pattern_type → FixSuggestion
+            // Prefer inline fix_suggestions (from LLM-enhanced response), fall back to separate fixes call
             val fixMap: Map<String, FixSuggestion> =
-                fixes?.suggestions?.associateBy { it.anti_pattern } ?: emptyMap()
+                if (r.fix_suggestions.isNotEmpty()) r.fix_suggestions.associateBy { it.anti_pattern }
+                else fixes?.suggestions?.associateBy { it.anti_pattern } ?: emptyMap()
 
             val critical = r.anti_patterns.filter { it.severity == "CRITICAL" }
             val high     = r.anti_patterns.filter { it.severity == "HIGH" }
@@ -335,6 +341,9 @@ class QualityToolWindowPanel : JPanel() {
         sb.ln("│  📍 Affected Layer    : ${ap.affected_layer}")
         sb.ln("│  🎯 Confidence        : ${(ap.confidence * 100).roundToInt()}%")
         sb.ln("│  📉 Severity          : ${ap.severity}")
+        if (ap.llm_validated) {
+            sb.ln("│  ✅ LLM Validated     : Confirmed by Gemini")
+        }
         if (fix != null && fix.impact_points != 0) {
             sb.ln("│  📉 Impact on Quality : ${fix.impact_points} points")
         }
@@ -346,8 +355,10 @@ class QualityToolWindowPanel : JPanel() {
         if (ap.files.size > 5) sb.ln("│     ... and ${ap.files.size - 5} more")
         sb.ln("│")
 
-        // Problem
-        val problemText = fix?.problem?.takeIf { it.isNotBlank() } ?: ap.description
+        // Problem — prefer LLM description (references actual code), then fix problem, then ap.description
+        val problemText = ap.llm_description.takeIf { it.isNotBlank() }
+            ?: fix?.problem?.takeIf { it.isNotBlank() }
+            ?: ap.description
         sb.ln("│  📖 Problem:")
         sb.ln("│     $problemText")
         sb.ln("│")