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import json |
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import csv |
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import io |
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import requests |
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import html |
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from bs4 import BeautifulSoup |
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from openai import OpenAI |
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client = OpenAI( |
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base_url="https://integrate.api.nvidia.com/v1", |
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api_key="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" |
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) |
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def clean_test_case_output(text): |
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""" |
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Cleans the output to handle HTML characters and unwanted tags. |
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""" |
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text = html.unescape(text) |
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soup = BeautifulSoup(text, 'html.parser') |
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cleaned_text = soup.get_text(separator="\n").strip() |
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return cleaned_text |
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def generate_testcases(user_story): |
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""" |
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Generates advanced QA test cases based on a provided user story by interacting |
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with Nvidia's llama model API. The prompt is refined for clarity, |
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and the output is processed for better quality. |
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:param user_story: A string representing the user story for which to generate test cases. |
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:return: A list of test cases in the form of dictionaries. |
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""" |
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few_shot_examples = """ |
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"if its not a DropBury or ODAC Portal User Storythen we perform testing in Tech360 iOS App" |
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"Generate as many as testcases possible minimum 6 ,maximum it can be anything" |
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"Understand the story thoroughly" |
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"If it's a DropBury or ODAC Portal User Story, then we perform testing in ODAC Portal" |
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""" |
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prompt = few_shot_examples + f"\nUser Story: {user_story}\n" |
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try: |
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completion = client.chat.completions.create( |
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model="meta/llama-3.1-405b-instruct", |
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messages=[ |
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{"role": "user", "content": prompt} |
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], |
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temperature=0.07, |
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top_p=0.5, |
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max_tokens=4096, |
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stream=True |
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) |
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test_cases_text = "" |
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for chunk in completion: |
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if chunk.choices[0].delta.content is not None: |
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test_cases_text += chunk.choices[0].delta.content |
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if test_cases_text.strip() == "": |
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return [{"test_case": "No test cases generated or output was empty."}] |
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test_cases_text = clean_test_case_output(test_cases_text) |
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try: |
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test_cases = json.loads(test_cases_text) |
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if isinstance(test_cases, list): |
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return test_cases |
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else: |
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return [{"test_case": test_cases_text}] |
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except json.JSONDecodeError: |
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return [{"test_case": test_cases_text}] |
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except requests.exceptions.RequestException as e: |
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print(f"API request failed: {str(e)}") |
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return [] |
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def export_test_cases(test_cases, format='json'): |
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if not test_cases: |
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return "No test cases to export." |
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structured_test_cases = [{'Test Case': case} for case in test_cases] |
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if format == 'json': |
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return json.dumps(test_cases, indent=4, separators=(',', ': ')) |
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elif format == 'csv': |
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if isinstance(test_cases, list) and isinstance(test_cases[0], dict): |
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output = io.StringIO() |
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csv_writer = csv.DictWriter(output, fieldnames=test_cases[0].keys(), quoting=csv.QUOTE_ALL) |
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csv_writer.writeheader() |
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csv_writer.writerows(test_cases) |
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return output.getvalue() |
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else: |
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raise ValueError("Test cases must be a list of dictionaries for CSV export.") |
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def save_test_cases_as_file(test_cases, format='json'): |
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if not test_cases: |
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return "No test cases to save." |
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if format == 'json': |
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with open('test_cases.json', 'w') as f: |
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json.dump(test_cases, f) |
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elif format == 'csv': |
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with open('test_cases.csv', 'w', newline='') as file: |
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dict_writer = csv.DictWriter(file, fieldnames=test_cases[0].keys()) |
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dict_writer.writeheader() |
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dict_writer.writerows(test_cases) |
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else: |
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return f"Unsupported format: {format}" |
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return f'{format} file saved' |
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