Merge pull request #139 from samt07/community-contributions-branch

Added test case automation solution

week1/community-contributions/testcase_automation.ipynb

@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "it1JLoxrSqO1",
+   "metadata": {
+    "id": "it1JLoxrSqO1"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install openai python-docx python-dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "950a084a-7f92-4669-af62-f07cb121da56",
+   "metadata": {
+    "id": "950a084a-7f92-4669-af62-f07cb121da56"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "from dotenv import load_dotenv\n",
+    "from IPython.display import Markdown, display, update_display\n",
+    "from openai import OpenAI\n",
+    "from docx import Document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab9f734f-ed6f-44f6-accb-594f9ca4843d",
+   "metadata": {
+    "id": "ab9f734f-ed6f-44f6-accb-594f9ca4843d"
+   },
+   "outputs": [],
+   "source": [
+    "class ReqDoc:\n",
+    "    def __init__(self, file_path):\n",
+    "        self.file_path = file_path\n",
+    "\n",
+    "    def extract(self):\n",
+    "        \"\"\"\n",
+    "        Reads the content of a .docx file and returns the paragraphs as a list of strings.\n",
+    "        \"\"\"\n",
+    "        try:\n",
+    "            # Check if the file exists\n",
+    "            if not os.path.exists(self.file_path):\n",
+    "                raise FileNotFoundError(f\"The file {self.file_path} was not found.\")\n",
+    "\n",
+    "            # Attempt to open and read the document\n",
+    "            doc = Document(self.file_path)\n",
+    "            text = \"\\n\".join([paragraph.text for paragraph in doc.paragraphs])\n",
+    "            return text\n",
+    "\n",
+    "        except FileNotFoundError as fnf_error:\n",
+    "            print(fnf_error)\n",
+    "            return None\n",
+    "        except Exception as e:\n",
+    "            print(f\"An error occurred: {e}\")\n",
+    "            return None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "008f485a-5718-48f6-b408-06eb6d59d7f9",
+   "metadata": {
+    "id": "008f485a-5718-48f6-b408-06eb6d59d7f9"
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize and constants\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "if api_key and api_key.startswith('sk-proj') and len(api_key)>10:\n",
+    "    print(\"API key looks good!\")\n",
+    "else:\n",
+    "    print(\"There might be a problem with your API key. Please check!\")\n",
+    "    \n",
+    "MODEL = 'gpt-4o-mini'\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6110ff3-74bc-430a-8051-7d86a216f0fb",
+   "metadata": {
+    "id": "b6110ff3-74bc-430a-8051-7d86a216f0fb"
+   },
+   "outputs": [],
+   "source": [
+    "#Set up system prompt for extracting just the requirements from the document\n",
+    "\n",
+    "req_doc_system_prompt = \"You are provided with a complete requirements specifications document. \\\n",
+    "You are able to decide which content from that document are related to actual requirements, identify each requirement as \\\n",
+    "functional or non-functional and list them all.\\n\"\n",
+    "req_doc_system_prompt += \"If the document is empty or do not contain requirements or if you cannot extract them, please respond as such.\\\n",
+    "Do not make up your own requirements. \\n\"\n",
+    "req_doc_system_prompt += \"You should respond in JSON as in this example:\"\n",
+    "req_doc_system_prompt += \"\"\"\n",
+    "{\n",
+    "    \"requirements\": [\n",
+    "        {\"RequirementNo\": \"FR-01\", \"Requirement Description\": \"description of this functional requirement goes here\"},\n",
+    "        {\"RequirementNo\": \"FR-02\": \"Requirement Description\": \"description of this functional requirement goes here\"},\n",
+    "        {\"RequirementNo\": \"NFR-01\": \"Requirement Description\": \"description of this non-functional requirement goes here\"},\n",
+    "        {\"RequirementNo\": \"NFR-02\": \"Requirement Description\": \"description of this non-functional requirement goes here\"}\n",
+    "    ]\n",
+    "}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20460e45-c1b7-4dc4-ab07-932235c19895",
+   "metadata": {
+    "id": "20460e45-c1b7-4dc4-ab07-932235c19895"
+   },
+   "outputs": [],
+   "source": [
+    "#Set up user prompt, sending in the requirements doc as input and calling the ReqDoc.extract function. Key to note here is the explicit instructions to\n",
+    "#respond in JSON format.\n",
+    "\n",
+    "def req_doc_user_prompt(doc):\n",
+    "    user_prompt = \"Here is the contents from a requirement document.\\n\"\n",
+    "    user_prompt += f\"{doc.extract()} \\n\"\n",
+    "    user_prompt += \"Please scan through the document and extract only the  actual requirements. For example, ignore sections or \\\n",
+    "paragraphs such as Approvers, table of contents and similar sections which are not really requirements.\\\n",
+    "You must respond in a JSON format\"\n",
+    "    user_prompt += \"If the content is empty, respond that there are no valid requirements you could extract and ask for a proper document.\\n\"\n",
+    "    user_prompt = user_prompt[:25_000] # Truncate if more than 25,000 characters\n",
+    "    return user_prompt\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a9f0f84-69a0-4971-a545-5bb40c2f9891",
+   "metadata": {
+    "id": "3a9f0f84-69a0-4971-a545-5bb40c2f9891"
+   },
+   "outputs": [],
+   "source": [
+    "#Function to call chatgpt-4o-mini model with the user and system prompts set above and returning the json formatted result obtained from chatgpt\n",
+    "\n",
+    "def get_requirements(doc):\n",
+    "    reqdoc = ReqDoc(doc)\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model=MODEL,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": req_doc_system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": req_doc_user_prompt(reqdoc)}\n",
+    "        ],\n",
+    "      response_format={\"type\": \"json_object\"}\n",
+    "    )\n",
+    "    result = response.choices[0].message.content\n",
+    "    return json.loads(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9bb04ef-78d3-4e0f-9ed1-59a961a0663e",
+   "metadata": {
+    "id": "f9bb04ef-78d3-4e0f-9ed1-59a961a0663e"
+   },
+   "outputs": [],
+   "source": [
+    "#Uncomment and run this if you want to see the extracted requriements in json format.\n",
+    "#get_requirements(\"reqdoc.docx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fe8618c-1dfe-4030-bad8-405731294c93",
+   "metadata": {
+    "id": "1fe8618c-1dfe-4030-bad8-405731294c93"
+   },
+   "source": [
+    "### Next, we will make another call to gpt-4o-mini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db2c1eb3-7740-43a4-9c0b-37b7e70c739b",
+   "metadata": {
+    "id": "db2c1eb3-7740-43a4-9c0b-37b7e70c739b"
+   },
+   "outputs": [],
+   "source": [
+    "#Set up system prompt to ask for test cases in table format\n",
+    "\n",
+    "system_prompt = \"You are an assitant that receives a list of functional and non functional requirements in JSON format. You are the expert in generating unit test cases for each requirement. \\\n",
+    "You will create as many different test cases as needed for each requirement and produce a result in a table. Order the table by requirement No. Provide clear details on test case pass criteria. \\\n",
+    "The table will contain the following columns. \\\n",
+    "1.S No\\\n",
+    "2.Requirement No\\\n",
+    "3.Requirement Description\\\n",
+    "4.Test Case ID\\\n",
+    "5.Test case summary\\\n",
+    "6.Test case description\\\n",
+    "7.Success criteria \\n\"\n",
+    "system_prompt += \"If you are provided with an empty list, ask for a proper requirement doc\\n\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4cd2bdf-e1bd-43ff-85fa-760ba39ed8c5",
+   "metadata": {
+    "id": "c4cd2bdf-e1bd-43ff-85fa-760ba39ed8c5"
+   },
+   "outputs": [],
+   "source": [
+    "# Set up user prompt passing in the req doc file. This in turn will call the get_requirements function, which will make a call to chatgpt.\n",
+    "\n",
+    "def get_testcase_user_prompt(reqdoc):\n",
+    "    user_prompt = \"You are looking at the following list of requirements. \\n\"\n",
+    "    user_prompt += f\"{get_requirements(reqdoc)}\\n\"\n",
+    "    user_prompt += \"Prepare unit test cases for each of these requirements in a table and send that table as response. \\n\"\n",
+    "    user_prompt += user_prompt[:25000]\n",
+    "    return user_prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59d859e2-e5bb-4bd6-ab59-5ad967d5d2e0",
+   "metadata": {
+    "id": "59d859e2-e5bb-4bd6-ab59-5ad967d5d2e0"
+   },
+   "outputs": [],
+   "source": [
+    "#This is the 2nd call to chatgpt to get test cases. display(Markdown) will take care of producing a neatly formatted table output.\n",
+    "def create_testcase_doc(reqdoc):\n",
+    "    stream = openai.chat.completions.create(\n",
+    "        model=MODEL,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": get_testcase_user_prompt(reqdoc)}\n",
+    "          ],\n",
+    "        stream=True\n",
+    "    )\n",
+    "    response = \"\"\n",
+    "    display_handle = display(Markdown(\"\"), display_id=True)\n",
+    "    for chunk in stream:\n",
+    "        response += chunk.choices[0].delta.content or ''\n",
+    "        response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
+    "        update_display(Markdown(response), display_id=display_handle.display_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0612d662-7047-4620-aa1c-2eb1c3d715cb",
+   "metadata": {
+    "id": "0612d662-7047-4620-aa1c-2eb1c3d715cb"
+   },
+   "outputs": [],
+   "source": [
+    "#The final piece of code. Provide the uploaded requirements filename below.\n",
+    "file_path = r\"reqdoc.docx\"\n",
+    "#print(file_path)\n",
+    "create_testcase_doc(file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82ae4371-22dd-4f2a-97c9-a70e0232a0aa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}