Test Result Analyzer

Automated testing is a critical part of the software development process, as it helps ensure the quality and reliability of a product. However, analyzing the results of automated tests can be a time-consuming and tedious task, especially if there are many test cases and a large number of test results to review. This is where the test result analyzer tool comes in handy.

How the Test Result Analyzer works:

The test result analyzer is a tool that aims to reduce the efforts required to analyze automated test results by using techniques such as term frequency-inverse document frequency (TF-IDF) and image comparison. It also proactively predicts the failure reason based on previous test execution data using a supervised learning approach. In this article, we will take a closer look at how the test result analyzer works and how it can benefit teams working on automated testing.

The test result analyzer tool works by analyzing the log file generated by the automated test runs using the TF-IDF technique. This involves identifying key words or phrases within the log file that are relevant to understanding the test results. For example, if a test case has failed, the tool may identify words such as “failure,” “error,” or “exception” as being important for understanding the cause of the failure.

In addition to analyzing the log file, the test result analyzer tool also uses image comparison to analyze failure screen shots. This involves comparing the expected results with the actual results to identify any visual differences. This can be particularly useful for identifying issues with layout, formatting, or other visual aspects of the product being tested.

Proactive failure prediction using supervised learning:
One of the key features of the test result analyzer tool is its ability to proactively predict the failure reason based on previous test execution data using a supervised learning approach. In this approach, the test execution data (such as logs, screenshots, and the reason for failure) is used as the input, and the failure reason is the output.

For example, if a new error occurs during a test run, the tool will add the test execution data (logs, screenshots, and the reason for failure) to its database. It will then use this data, along with data from previous test runs, to train a model that can predict the failure reason for future test runs. This can be particularly useful for identifying patterns or trends that may indicate the cause of future failures.

Sample Code

Here is some code that demonstrates how to compare a test automation log file against a list of log files labeled previously and return the closest matching log file using the TF-IDF vectorization method and cosine similarity:

import os
import re
import math
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def get_log_file_text(filepath):
    """Reads the contents of a log file and returns it as a string"""
    with open(filepath, 'r') as f:
        log_text = f.read()
    return log_text

def preprocess_text(text):
    """Cleans and preprocesses the text by removing punctuation and lowercasing"""
    # Remove punctuation
    text = re.sub(r'[^\w\s]', '', text)
    # Lowercase
    text = text.lower()
    return text

def compare_logs(test_log_file, log_files, labels):
    """Compares the test log file against the list of log files using tf-idf vectorization and cosine similarity"""
    # Read the text from the test log file
    test_log_text = get_log_file_text(test_log_file)
    # Preprocess the text
    test_log_text = preprocess_text(test_log_text)

    # Read the text from the other log files and preprocess it
    log_texts = [preprocess_text(get_log_file_text(f)) for f in log_files]

    # Combine the test log text and the log texts into a single list
    texts = [test_log_text] + log_texts

    # Use TfidfVectorizer to create a vector representation of the text
    vectorizer = TfidfVectorizer()
    vectors = vectorizer.fit_transform(texts)

    # Get the vector for the test log file
    test_log_vector = vectors[0]

    # Compute the cosine similarity between the test log file and each of the other log files
    similarities = []
    for i in range(1, len(log_files)):
        log_vector = vectors[i]
        similarity = cosine_similarity(test_log_vector, log_vector)[0][0]
        similarities.append(similarity)

    # Find the index of the log file with the highest similarity to the test log file
    max_similarity_index = similarities.index(max(similarities))

    # Return the label of the log file with the highest similarity
    return labels[max_similarity_index]

# Example usage
test_log_file = '/path/to/test/log/file.log'
log_files = ['/path/to/log/file1.log', '/path/to/log/file2.log', '/path/to/log/file3.log']
labels = ['label1', 'label2', 'label3']
closest_matching_log = compare_logs(test_log_file, log_files, labels)
print(closest_matching_log)

This code reads the text from each log file, preprocesses it by removing punctuation and lowercasing it, and then uses the TfidfVectorizer to create a vector representation of the text. It then uses the cosine_similarity function from scikit-learn to compute the cosine similarity between the test log file and each of the other log files. The resulting similarities are returned as a list of floating point values.

To compare an image against a list of images using Python, you can use the OpenCV library. Here is some example code that demonstrates how to compare a test failure screenshot against a list of screenshots labeled previously and return the closest matching screenshot:

import cv2
import numpy as np

def compare_screenshots(test_screenshot, screenshots, labels):
    """Compares the test screenshot against the list of screenshots using OpenCV's matchTemplate function and returns the closest matching screenshot"""
    # Read the test screenshot image
    test_screenshot_image = cv2.imread(test_screenshot)

    # Read the other screenshot images
    screenshot_images = [cv2.imread(f) for f in screenshots]

    # Initialize a list to store the similarities
    similarities = []

    # Iterate over the screenshot images
    for screenshot_image in screenshot_images:
        # Use OpenCV's matchTemplate function to compare the test screenshot against the current screenshot image
        result = cv2.matchTemplate(test_screenshot_image, screenshot_image, cv2.TM_CCOEFF_NORMED)
        # Get the maximum value from the result (which represents the similarity)
        similarity = np.max(result)
        # Add the similarity to the list
        similarities.append(similarity)

    # Find the index of the screenshot with the highest similarity to the test screenshot
    max_similarity_index = similarities.index(max(similarities))

    # Return the label of the screenshot with the highest similarity
    return labels[max_similarity_index]

# Example usage
test_screenshot = '/path/to/test/screenshot.png'
screenshots = ['/path/to/screenshot1.png', '/path/to/screenshot2.png', '/path/to/screenshot3.png']
labels = ['label1', 'label2', 'label3']
closest_matching_screenshot = compare_screenshots(test_screenshot, screenshots, labels)
print(closest_matching_screenshot)

This code reads the test screenshot and the other screenshots as images using OpenCV’s imread function, and then uses the matchTemplate function to compare the test screenshot against each of the other screenshots. The matchTemplate function returns a matrix of values representing the similarity between the images, and the code gets the maximum value from the matrix to determine the overall similarity between the images. Finally, it finds the index of the screenshot with the highest similarity to the test screenshot and returns the corresponding label.

Benefits of using the Test Result Analyzer tool:
There are several benefits to using the test result analyzer tool, including:
• Reduced effort: By using techniques such as TF-IDF and image comparison, the test result analyzer tool can help teams analyze test results more efficiently, potentially saving them time and effort in the process.
• Improved understanding of test results: The tool’s ability to identify key words and visual differences can help teams better understand the results of their automated test runs, making it easier to identify and fix issues.
• Proactive failure prediction: The tool’s ability to proactively predict the failure reason based on previous test execution data can help teams anticipate and prevent future failures, improving the overall reliability of the product.

Conclusion:
The test result analyzer tool is a valuable tool for teams working on automated testing. By using techniques such as TF-IDF and image comparison, and by proactively predicting failure reasons using a supervised learning approach, it can help teams analyze test results more efficiently and effectively, ultimately improving the quality and reliability of the product.

#testresultanalyzer #automatedtesting #tfidf #imagecomparison #supervisedlearning #qualityassurance #tests #failureprediction #testingtools #QA #ai4testing