Analyzing and Visualizing Test Results
Analyzing test results is a crucial step in any automated testing workflow. When you run a suite of automated tests, you typically generate a large amount of result data—such as which tests passed, which failed, and any errors encountered. Simply reading through raw logs or result tables can make it difficult to quickly spot trends or recurring problems. Visualization transforms this data into clear, graphical summaries, enabling you to quickly pinpoint areas that need attention. By using visual tools like bar charts, you can immediately see patterns in test outcomes, such as a spike in failures or a consistently failing component, allowing for faster troubleshooting and more informed decision-making.
1234567891011121314151617181920212223242526import matplotlib.pyplot as plt # Simulated test result data test_results = [ {"name": "test_login", "result": "pass"}, {"name": "test_logout", "result": "fail"}, {"name": "test_signup", "result": "pass"}, {"name": "test_profile", "result": "fail"}, {"name": "test_reset_password", "result": "pass"}, {"name": "test_update_email", "result": "pass"}, {"name": "test_delete_account", "result": "fail"}, ] # Summarize pass/fail counts pass_count = sum(1 for t in test_results if t["result"] == "pass") fail_count = sum(1 for t in test_results if t["result"] == "fail") # Create bar chart labels = ["pass", "fail"] counts = [pass_count, fail_count] plt.bar(labels, counts) plt.title("Test Results Summary") plt.xlabel("Result") plt.ylabel("Count") plt.show()
When you interpret a bar chart like the one above, look at the relative heights of the "pass" and "fail" bars. A taller "fail" bar signals a potential issue with the system under test or the test suite itself. If most tests pass but a few consistently fail, you can focus your investigation on those areas. Visualizations quickly highlight trends—such as a sudden increase in failures after a recent code change—making it easier to identify and prioritize problem areas for further analysis or debugging. Always use these graphical summaries as a starting point for deeper investigation into your test results.
123456789101112131415import matplotlib.pyplot as plt # Using the same summarized counts from before labels = ["pass", "fail"] counts = [pass_count, fail_count] colors = ["green", "red"] plt.bar(labels, counts, color=colors) plt.title("Test Results Summary") plt.xlabel("Result") plt.ylabel("Count") plt.ylim(0, max(counts) + 1) for i, v in enumerate(counts): plt.text(i, v + 0.05, str(v), ha="center", fontweight="bold") plt.show()
1. What type of chart is useful for visualizing test pass/fail counts?
2. Why visualize test results?
3. Fill in the blank: plt.bar(["pass", "fail"], counts, ___="green") sets the bar color.
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Analyzing test results is a crucial step in any automated testing workflow. When you run a suite of automated tests, you typically generate a large amount of result data—such as which tests passed, which failed, and any errors encountered. Simply reading through raw logs or result tables can make it difficult to quickly spot trends or recurring problems. Visualization transforms this data into clear, graphical summaries, enabling you to quickly pinpoint areas that need attention. By using visual tools like bar charts, you can immediately see patterns in test outcomes, such as a spike in failures or a consistently failing component, allowing for faster troubleshooting and more informed decision-making.
1234567891011121314151617181920212223242526import matplotlib.pyplot as plt # Simulated test result data test_results = [ {"name": "test_login", "result": "pass"}, {"name": "test_logout", "result": "fail"}, {"name": "test_signup", "result": "pass"}, {"name": "test_profile", "result": "fail"}, {"name": "test_reset_password", "result": "pass"}, {"name": "test_update_email", "result": "pass"}, {"name": "test_delete_account", "result": "fail"}, ] # Summarize pass/fail counts pass_count = sum(1 for t in test_results if t["result"] == "pass") fail_count = sum(1 for t in test_results if t["result"] == "fail") # Create bar chart labels = ["pass", "fail"] counts = [pass_count, fail_count] plt.bar(labels, counts) plt.title("Test Results Summary") plt.xlabel("Result") plt.ylabel("Count") plt.show()
When you interpret a bar chart like the one above, look at the relative heights of the "pass" and "fail" bars. A taller "fail" bar signals a potential issue with the system under test or the test suite itself. If most tests pass but a few consistently fail, you can focus your investigation on those areas. Visualizations quickly highlight trends—such as a sudden increase in failures after a recent code change—making it easier to identify and prioritize problem areas for further analysis or debugging. Always use these graphical summaries as a starting point for deeper investigation into your test results.
123456789101112131415import matplotlib.pyplot as plt # Using the same summarized counts from before labels = ["pass", "fail"] counts = [pass_count, fail_count] colors = ["green", "red"] plt.bar(labels, counts, color=colors) plt.title("Test Results Summary") plt.xlabel("Result") plt.ylabel("Count") plt.ylim(0, max(counts) + 1) for i, v in enumerate(counts): plt.text(i, v + 0.05, str(v), ha="center", fontweight="bold") plt.show()
1. What type of chart is useful for visualizing test pass/fail counts?
2. Why visualize test results?
3. Fill in the blank: plt.bar(["pass", "fail"], counts, ___="green") sets the bar color.
Obrigado pelo seu feedback!
