GCI104 AI AvoidCreatingTensorUsingNumpyOrNativePython : Add IT test and remove utils class

Co-authored-by: DataLabGroupe-CreditAgricole <[email protected]>

Author: cleophass

src/it/java/org/greencodeinitiative/creedengo/python/integration/tests/GCIRulesIT.java

@@ -366,6 +366,22 @@ void testGCI103(){
 
         checkIssuesForFile(filePath, ruleId, ruleMsg, startLines, endLines, SEVERITY, TYPE, EFFORT_1MIN);  
     }
+
+    @Test
+    void testGCI104() {
+
+        String filePath = "src/avoidCreatingTensorUsingNumpyOrNativePython.py";
+        String ruleId = "creedengo-python:GCI104";
+        String ruleMsg = "Directly create tensors as torch.Tensor instead of using numpy functions.";
+        int[] startLines = new int[]{
+            5, 15, 19, 24
+        };
+        int[] endLines = new int[]{
+            5, 15, 19, 24
+        };
+
+        checkIssuesForFile(filePath, ruleId, ruleMsg, startLines, endLines, SEVERITY, TYPE, EFFORT_10MIN);
+    }
 
     @Test
     void testGCI105() {

src/it/test-projects/creedengo-python-plugin-test-project/src/avoidCreatingTensorUsingNumpyOrNativePython.py

@@ -0,0 +1,24 @@
+import numpy as np
+import torch
+
+def non_compliant_random_rand():
+    tensor = torch.tensor(np.random.rand(1000, 1000)) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}
+
+def compliant_random_rand():
+    tensor = torch.rand([1000, 1000])
+
+def compliant_zeros():
+    tensor_ = torch.zeros(1, 2)
+    print(tensor_)
+
+def non_compliant_zeros():
+    tensor_ = torch.IntTensor(np.zeros(1, 2)) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}
+    print(tensor_)
+
+def non_compliant_eye():
+    tensor = torch.cuda.LongTensor(np.eye(5)) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}
+
+def non_compliant_ones():
+    import numpy
+    from torch import FloatTensor
+    tensor = FloatTensor(data=np.ones(shape=(1, 5))) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}

src/main/java/org/greencodeinitiative/creedengo/python/checks/AvoidCreatingTensorUsingNumpyOrNativePython.java

@@ -17,7 +17,7 @@
  */
 package org.greencodeinitiative.creedengo.python.checks;
 
-import org.sonar.check.Priority;
+import org.greencodeinitiative.creedengo.python.utils.UtilsAST;
 import org.sonar.check.Rule;
 import org.sonar.plugins.python.api.PythonSubscriptionCheck;
 import org.sonar.plugins.python.api.tree.Tree;
@@ -33,7 +33,6 @@
 @Rule(key = "GCI104")
 public class AvoidCreatingTensorUsingNumpyOrNativePython extends PythonSubscriptionCheck {
 
-  public static final String RULE_KEY = "P5";
   private static final String dataArgumentName = "data";
   private static final int dataArgumentPosition = 0;
   private static final Map<String, String> torchOtherFunctionsMapping = Map.ofEntries(
@@ -65,19 +64,19 @@ public class AvoidCreatingTensorUsingNumpyOrNativePython extends PythonSubscript
     "torch.cuda.CharTensor", "torch.cuda.ShortTensor",
     "torch.cuda.IntTensor", "torch.cuda.LongTensor",
     "torch.cuda.BoolTensor");
-  protected static final String MESSAGE = "Directly create tensors as torch.Tensor. Use %s instead of %s.";
+  protected static final String MESSAGE = "Directly create tensors as torch.Tensor instead of using numpy functions.";
 
   @Override
   public void initialize(Context context) {
     context.registerSyntaxNodeConsumer(Tree.Kind.CALL_EXPR, ctx -> {
       CallExpression callExpression = (CallExpression) ctx.syntaxNode();
-      if (torchTensorConstructors.contains(Utils.getQualifiedName(callExpression))) {
-        RegularArgument tensorCreatorArgument = Utils.nthArgumentOrKeyword(dataArgumentPosition, dataArgumentName, callExpression.arguments());
+      if (torchTensorConstructors.contains(UtilsAST.getQualifiedName(callExpression))) {
+        RegularArgument tensorCreatorArgument = UtilsAST.nthArgumentOrKeyword(dataArgumentPosition, dataArgumentName, callExpression.arguments());
         if (tensorCreatorArgument != null) {
           if (tensorCreatorArgument.expression().is(CALL_EXPR)) {
-            String functionQualifiedName = Utils.getQualifiedName((CallExpression) tensorCreatorArgument.expression());
+            String functionQualifiedName = UtilsAST.getQualifiedName((CallExpression) tensorCreatorArgument.expression());
             if (torchOtherFunctionsMapping.containsKey(functionQualifiedName)) {
-              ctx.addIssue(callExpression, String.format(MESSAGE, torchOtherFunctionsMapping.get(functionQualifiedName), functionQualifiedName));
+              ctx.addIssue(callExpression, MESSAGE);
             }
           }
         }

src/main/java/org/greencodeinitiative/creedengo/python/checks/Utils.java

@@ -52,6 +52,19 @@ public static String getQualifiedName(CallExpression callExpression) {
                 .orElse("");
     }
 
+    /**
+     * Retrieves the variable name from the given SubscriptionContext.
+     *
+     * This method traverses the syntax tree of the provided context to locate
+     * the nearest assignment statement. If an assignment statement is found,
+     * it extracts the name of the variable on the left-hand side of the assignment.
+     *
+     * @param context The SubscriptionContext containing the syntax node to analyze.
+     *                It may be null or contain a null syntax node, in which case
+     *                the method returns null.
+     * @return The name of the variable on the left-hand side of the assignment
+     *         statement, or null if no valid variable name can be determined.
+     */
     public static String getVariableName(SubscriptionContext context) {
 
         if (context == null || context.syntaxNode() == null) {

src/main/java/org/greencodeinitiative/creedengo/python/utils/UtilsAST.java

@@ -16,6 +16,7 @@
 		"GCI101",
 		"GCI102",
 		"GCI103",
+		"GCI104",
 		"GCI105",
 		"GCI106",
 		"GCI107",

src/main/resources/org/greencodeinitiative/creedengo/python/creedengo_way_profile.json

@@ -2,7 +2,7 @@
 import torch
 
 def non_compliant_random_rand():
-    tensor = torch.tensor(np.random.rand(1000, 1000)) # Noncompliant {{Directly create tensors as torch.Tensor. Use torch.rand instead of numpy.random.rand.}}
+    tensor = torch.tensor(np.random.rand(1000, 1000)) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}
 
 def compliant_random_rand():
     tensor = torch.rand([1000, 1000])
@@ -12,13 +12,13 @@ def compliant_zeros():
     print(tensor_)
 
 def non_compliant_zeros():
-    tensor_ = torch.IntTensor(np.zeros(1, 2)) # Noncompliant {{Directly create tensors as torch.Tensor. Use torch.zeros instead of numpy.zeros.}}
+    tensor_ = torch.IntTensor(np.zeros(1, 2)) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}
     print(tensor_)
 
 def non_compliant_eye():
-    tensor = torch.cuda.LongTensor(np.eye(5)) # Noncompliant {{Directly create tensors as torch.Tensor. Use torch.eye instead of numpy.eye.}}
+    tensor = torch.cuda.LongTensor(np.eye(5)) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}
 
 def non_compliant_ones():
     import numpy
     from torch import FloatTensor
-    tensor = FloatTensor(data=np.ones(shape=(1, 5))) # Noncompliant {{Directly create tensors as torch.Tensor. Use torch.ones instead of numpy.ones.}}
+    tensor = FloatTensor(data=np.ones(shape=(1, 5))) # Noncompliant {{Directly create tensors as torch.Tensor instead of using numpy functions.}}