small changes

Author: Rangeet Pan

cldk/analysis/java/codeanalyzer/codeanalyzer.py

@@ -770,7 +770,7 @@ def __call_graph_using_symbol_table(self,
     def __raw_call_graph_using_symbol_table(self,
                                             qualified_class_name: str,
                                             method_signature: str,
-                                            cg: list[JGraphEdges] = []) -> list[JGraphEdgesST]:
+                                            cg: list[JGraphEdgesST] = []) -> list[JGraphEdgesST]:
         """
         Generates call graph using symbol table information
         Args:
@@ -779,7 +779,7 @@ def __raw_call_graph_using_symbol_table(self,
             cg: call graph
 
         Returns:
-            list[JGraphEdges]: list of call edges
+            list[JGraphEdgesST]: list of call edges
         """
         source_method_details = self.get_method(qualified_class_name=qualified_class_name,
                                                 method_signature=method_signature)

docs/examples/java/generate_unit_tests.ipynb

@@ -3,11 +3,11 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Generating unit tests for code is a very tedious task and often takes a significant effort from the developers to write good test cases. There are various tools that are available for automated test generation, such as, EvoSuite, which uses evolutionary algorithm to generate test cases. However, the test cases that are being generated are not natural and often developers do not prefer to add them to their test suite. Whereas, Large Language Models (LLM) being trained with developer-written code, it has better affinity towards generating more natural code--more readable, maintainable code. In this excercise, we will show we can leverage LLMs to generate test cases with the help of CLDK. \n",
+    "Generating unit tests for code is a very tedious task and often takes a significant effort from the developers to write good test cases. There are various tools that are available for automated test generation, such as EvoSuite, which uses evolutionary algorithms to generate test cases. However, the test cases that are being generated are not natural and often developers do not prefer to add them to their test suite. Whereas Large Language Models (LLM) being trained with developer-written code it has a better affinity towards generating more natural code--more readable, maintainable code. In this excercise, we will show we can leverage LLMs to generate test cases with the help of CLDK. \n",
     "\n",
-    "For simplicity, we will cover certain aspects of test generation and provide some context information to LLM for better quality of test cases. In this excercise, we will generate unit test for non-private method from a Java class and provide the focal method body and the signature of all the constructors of the class so that LLM can understand how to create object of the focal class during the setup phase of the tests. Also, we will ask LLMs to generate ```N``` number of test cases, where ```N``` is the cyclomatic complexity of the focal method. The intuition is that one test may not be sufficient for covering fairly complex method and cyclomatic complexity score can provide some guidance towards that. \n",
+    "For simplicity, we will cover certain aspects of test generation and provide some context information to LLM for better quality of test cases. In this exercise, we will generate a unit test for a non-private method from a Java class and provide the focal method body and the signature of all the constructors of the class so that LLM can understand how to create an object of the focal class during the setup phase of the tests. Also, we will ask LLMs to generate ```N``` number of test cases, where ```N``` is the cyclomatic complexity of the focal method. The intuition is that one test may not be sufficient for covering fairly complex methods, and a cyclomatic complexity score can provide some guidance towards that. \n",
     "\n",
-    "(Step 1) First, we will import all the neccessary libraries"
+    "(Step 1) First, we will import all the necessary libraries"
    ],
    "metadata": {
     "collapsed": false