File size: 6,938 Bytes
df11144 8b55d5e df11144 2b8f81c df11144 8b55d5e 7668197 df11144 439d8a3 df11144 2b8f81c df11144 439d8a3 df11144 2b8f81c df11144 38c59e6 df11144 439d8a3 df11144 a477e6a df11144 439d8a3 df11144 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
import json
import csv
import io
import requests
import html # For escaping HTML characters
from bs4 import BeautifulSoup
from openai import OpenAI
# Initialize OpenAI API with Nvidia's llama model
client = OpenAI(
base_url="https://integrate.api.nvidia.com/v1",
api_key="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"
)
def clean_test_case_output(text):
"""
Cleans the output to handle HTML characters and unwanted tags.
"""
text = html.unescape(text) # Unescape HTML entities
soup = BeautifulSoup(text, 'html.parser') # Use BeautifulSoup to handle HTML tags
cleaned_text = soup.get_text(separator="\n").strip() # Remove tags and handle newlines
return cleaned_text
def generate_testcases(user_story):
"""
Generates advanced QA test cases based on a provided user story by interacting
with Nvidia's llama model API. The prompt is refined for clarity,
and the output is processed for better quality.
:param user_story: A string representing the user story for which to generate test cases.
:return: A list of test cases in the form of dictionaries.
"""
# Few-shot learning examples to guide the model
few_shot_examples = """
Example 1:
User Story:
Ensure the "Key Details" feature is enabled in the backend.
1. Open the Tech 360 app.
2. Click on the Search tab.
3. Enter the account number and click on Enter.
4. Choose the account from the Search result.
5. Verify that the "Key Details" button is displayed under the recommendation section.
6. Clicking on "Key Details" should navigate to the key details screen.
Test Case:
Test Case: Verify "Key Details" Feature in Tech 360 App
Steps:
1. Launch the Tech 360 app.
2. Go to the Search tab.
3. Enter a valid account number and click Enter.
4. Select an account from the search results.
5. Ensure the "Key Details" button is displayed under the recommendation section.
6. Click on "Key Details" and verify that it navigates to the Key Details screen.
Expected Result: User is navigated to the Key Details screen successfully.
Example 2:
User Story:
Open the Tech 360 app. Go On-Job on a Wi-Fi Ready Preinstall Job. Wait for PHT to finish. Go to the Device Details Page for XB7 and ONU.
Test Case:
Test Case: Verify Device Details Page for XB7 and ONU in Wi-Fi Ready Preinstall Job
Steps:
1. Launch the Tech 360 app.
2. Go On-Job on a Wi-Fi Ready Preinstall Job.
3. Wait for PHT to finish.
4. Navigate to the Device Details Page for XB7 and ONU.
Expected Result: Device details for XB7 and ONU are displayed correctly.
Example 3:
User Story:
Open the Tech 360 app. Go On-Job on a Wi-Fi Ready Preinstall Job. Initiate Add/Remove/Swap Flows for ONU and XB7.
Test Case:
Test Case: Verify Add/Remove/Swap Flows for ONU and XB7 in Wi-Fi Ready Preinstall Job
Steps:
1. Launch the Tech 360 app.
2. Go On-Job on a Wi-Fi Ready Preinstall Job.
3. Initiate Add/Remove/Swap flows for ONU and XB7.
Expected Result: Add/Remove/Swap flows for ONU and XB7 are initiated and processed successfully.
"""
# Combine the few-shot examples with the user story for the model to process
prompt = few_shot_examples + f"\nUser Story: {user_story}\n"
try:
# Call the Nvidia llama API with the refined prompt
completion = client.chat.completions.create(
model="meta/llama-3.1-405b-instruct", # Using llama3.1 405b model
messages=[
{"role": "user", "content": prompt}
],
temperature=0.06, # Further lowering temperature for precise and deterministic output
top_p=0.5, # Prioritize high-probability tokens even more
max_tokens=4096, # Increase max tokens to allow longer content
stream=True # Streaming the response for faster retrieval
)
# Initialize an empty string to accumulate the response
test_cases_text = ""
# Accumulate the response from the streaming chunks
for chunk in completion:
if chunk.choices[0].delta.content is not None:
test_cases_text += chunk.choices[0].delta.content
# Ensure the entire response is captured before cleaning
if test_cases_text.strip() == "":
return [{"test_case": "No test cases generated or output was empty."}]
# Clean the output by unescaping HTML entities and replacing <br> tags
test_cases_text = clean_test_case_output(test_cases_text)
try:
# Try to parse the output as JSON, assuming the model returns structured test cases
test_cases = json.loads(test_cases_text)
if isinstance(test_cases, list):
return test_cases # Return structured test cases
else:
return [{"test_case": test_cases_text}] # Return as a list with the text wrapped in a dict
except json.JSONDecodeError:
# Fallback: return the raw text if JSON parsing fails
return [{"test_case": test_cases_text}]
except requests.exceptions.RequestException as e:
print(f"API request failed: {str(e)}")
return []
# Add options for multiple test case formats
def export_test_cases(test_cases, format='json'):
if not test_cases:
return "No test cases to export."
# Convert test cases (which are currently strings) into a structured format for CSV
structured_test_cases = [{'Test Case': case} for case in test_cases]
if format == 'json':
# Improve JSON export to be line-by-line formatted
return json.dumps(test_cases, indent=4, separators=(',', ': ')) # More readable format
elif format == 'csv':
if isinstance(test_cases, list) and isinstance(test_cases[0], dict):
output = io.StringIO()
csv_writer = csv.DictWriter(output, fieldnames=test_cases[0].keys(), quoting=csv.QUOTE_ALL)
csv_writer.writeheader()
csv_writer.writerows(test_cases)
return output.getvalue()
else:
raise ValueError("Test cases must be a list of dictionaries for CSV export.")
# 2. Save test cases as a downloadable file
def save_test_cases_as_file(test_cases, format='json'):
if not test_cases:
return "No test cases to save."
if format == 'json':
with open('test_cases.json', 'w') as f:
json.dump(test_cases, f)
elif format == 'csv':
with open('test_cases.csv', 'w', newline='') as file:
dict_writer = csv.DictWriter(file, fieldnames=test_cases[0].keys())
dict_writer.writeheader()
dict_writer.writerows(test_cases)
else:
return f"Unsupported format: {format}"
return f'{format} file saved'
|