doc: add lisa case training data.

It generate a jsonl file for llm training purpose. The input is test case descriptions of a test file, output is the test case file content.

docs/conf.py

@@ -20,7 +20,11 @@ root_dir = Path(__file__).parent.parent
 sys.path.insert(0, str(root_dir))
 sys.path.insert(0, str(root_dir / "docs"))
 
-from tools import update_file, update_summary  # type: ignore # noqa: E402
+from tools import (  # type: ignore # noqa: E402
+    update_file,
+    update_finetune_data,
+    update_summary,
+)
 
 # -- Project information -----------------------------------------------------
 
@@ -87,3 +91,4 @@ base_path = Path(__file__).parent
 
 update_summary()
 update_file()
+update_finetune_data()

docs/tools/__init__.py

@@ -1,7 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from .finetune_gen import update_finetune_data
 from .test_spec_gen import update_file
 from .test_summary_gen import update_summary
 
-__all__ = ["update_summary", "update_file"]
+__all__ = ["update_summary", "update_file", "update_finetune_data"]

docs/tools/finetune_gen.py

@@ -0,0 +1,84 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""
+This script generates fine-tune data for AI models.
+"""
+import ast
+import json
+import os
+from pathlib import Path
+from typing import Dict
+
+from .doc_generator import TESTS, ClassVisitor, FuncVisitor, extract_metadata, load_path
+
+base_path = Path(__file__).parent
+file_path = (base_path / "../_build/html/finetune_cases.jsonl").resolve()
+system_prompt = "You are a helpful assistant to write Python code for linux validation."
+user_prompt = "Please write LISA test cases based on the test case descriptions.\n\n\n"
+
+
+def update_finetune_data() -> None:
+    """
+    Updates (rewrites) fine tune data for AI models.
+    """
+    data = load_path(TESTS)
+    test_paths = [(base_path / Path(x.get("value", ""))).resolve() for x in data]
+
+    file_path_parent = file_path.parent
+    if not os.path.exists(file_path_parent):
+        os.makedirs(file_path_parent)
+
+    with open(file_path, "w", encoding="utf-8") as f:
+        for test_path in test_paths:
+            for root, _, files in os.walk(test_path):
+                for file in files:
+                    if file.endswith(".py"):
+                        descriptions = "test suite description:\n"
+                        test_name = Path(root) / file
+                        tree = ast.parse(
+                            test_name.read_text(encoding="utf-8"),
+                            filename=str(test_name),
+                        )
+                        cls_visitor = ClassVisitor()
+                        func_visitor = FuncVisitor()
+                        cls_visitor.visit(tree)
+                        func_visitor.visit(tree)
+
+                        for suite_metadata in extract_metadata(
+                            cls_visitor.get_suites()
+                        ):
+                            descriptions += _get_description(suite_metadata, True)
+                            descriptions += "\n\n"
+                            for case in extract_metadata(func_visitor.get_cases()):
+                                descriptions += "test case:\n"
+                                descriptions += _get_description(case)
+                                descriptions += "\n\n"
+
+                        source = test_name.read_text(encoding="utf-8")
+
+                        f.write(
+                            json.dumps(
+                                {
+                                    "messages": [
+                                        {"role": "system", "content": system_prompt},
+                                        {
+                                            "role": "user",
+                                            "content": user_prompt + descriptions,
+                                        },
+                                        {"role": "assistant", "content": source},
+                                    ]
+                                }
+                            )
+                            + "\n"
+                        )
+
+
+def _get_description(metadata: Dict[str, str], is_suite: bool = False) -> str:
+    text = metadata["description"].split("\n")
+
+    # filter out empty lines
+    res = filter(lambda line: not line.isspace() and line != "", text)
+    text = list(res)
+
+    return "\n".join(text)