🐛Buggy Questions

Tell me about the best bug you ever found.

Me: One of the most severe bugs I encountered was with a request approval workflow. When an employee submitted a request for manager approval, the request was incorrectly going to the employee who submitted it, not the manager.

Interviewer: What made it severe?

Me: The impact was high severity because:

• Managers were unaware of requests requiring their approval

• Employees assumed their requests were being reviewed when they were not

• Requests needing timely action were not actually being addressed

• A critical workflow was essentially broken

Interviewer: What challenges did you face resolving the bug?

Me: The main challenges were:

✔ Determining if it was a server, code, or database issue

✔ Ensuring all current and future requests are routed correctly

✔ Restoring user confidence in the request/approval workflow

Interviewer: How did you solve it?

Me: After thorough debugging, a minor code issue incorrectly routing requests were identified and fixed. Comprehensive regression testing verified the workflow worked properly across all use cases.

Interviewer: Why was it considered high severity?

Me: The severity was high because:

✔ A major workflow functionality was broken

✔ The impact affected all managers and employees that used the requested feature

✔ Time-sensitive requests were likely impacted

The business impact and consequences of the broken functionality made this a high-priority, high-severity bug to resolve urgently.

Interviewer: Good insights. Thanks for sharing that example in so much detail - it helps me understand your testing and problem-solving approach.

Can you provide an example of a bug you found while testing the CNBC API?

🗣️ Interviewee: Sure, I can give you an example of a bug I found while testing the CNBC API.

During my testing, I was verifying the response of the CNBC API for the "Top News" endpoint. This endpoint is used to retrieve the latest top news articles from CNBC's news feed. While testing, I noticed that some of the news articles returned by the API were not properly formatted, and the data was missing some key information, such as the article's author and publication date.

I reported this issue to the development team, and they investigated the problem. They discovered that the issue was caused by a bug in the API's code, which was failing to parse the news articles correctly from the news feed.

The development team quickly fixed the bug and deployed the updated API to the test environment. I then retested the "Top News" endpoint and verified that all the articles were now properly formatted and contained all the necessary information, including the author and publication date.

This bug was significant because it affected the accuracy and completeness of the news articles returned by the API. Without the proper formatting and information, the articles were essentially useless to the end-users who relied on the API for accessing the latest news from CNBC.

By identifying and reporting this bug, I helped ensure that the API's functionality was restored quickly and that users could continue to rely on it for accessing accurate and complete news articles from CNBC.

Can you provide another example of a bug you found while testing the CNBC API?

🗣️ Interviewee: Sure, I can give you another example of a bug I discovered while testing the CNBC API.

During my testing, I was verifying the response of the "Stock Quote" endpoint, which is used to retrieve real-time stock quotes for a specific stock symbol. While testing, I noticed that the API was returning incorrect stock prices for certain stock symbols.

I reported this issue to the development team, and they investigated the problem. They discovered that the issue was caused by a bug in the API's code, which was failing to retrieve the correct data from the stock exchange's servers.

The development team quickly fixed the bug and deployed the updated API to the test environment. I then retested the "Stock Quote" endpoint and verified that the stock prices were now correct for all stock symbols.

This bug was significant because it affected the accuracy of the stock prices returned by the API. Users who relied on the API for real-time stock prices would have received incorrect information, which could have led to significant financial losses.

By identifying and reporting this bug, I helped ensure that the API's functionality was restored quickly and that users could continue to rely on it for accessing accurate and reliable stock prices.

Can you give me a tough example of a bug you found while testing the CNBC API?

🗣️ Interviewee: Sure, I can give you an example of a complex bug that I discovered while testing the CNBC API.

During my testing, I was verifying the response of the "Market Data" endpoint, which is used to retrieve real-time market data for various financial instruments, such as stocks, bonds, and futures. While testing, I noticed that the API was returning inconsistent market data for certain stock symbols, even though the data was correct on the stock exchange's website.

I investigated the issue further and found that the problem was related to how the API was handling the time zones of the market data. The API was using a different time zone than the stock exchange's website, causing the data to be off for several hours.

I reported this issue to the development team, and they investigated the problem. They found that the issue was not straightforward to fix because it required modifying the API's code to handle time zones correctly, which would require significant changes to the existing codebase.

The development team worked on a solution, and after several iterations of testing and debugging, they were able to fix the issue and deploy the updated API to the test environment. I then retested the "Market Data" endpoint and verified that the market data was now consistent with the data on the stock exchange's website.

This bug was challenging because it required a deep understanding of how the API was handling time zones and how it was interacting with the stock exchange's servers. It also required significant changes to the API's codebase, which could have introduced new bugs or issues if not done carefully.

By identifying and reporting this bug, I helped ensure that the API's functionality was restored quickly and that users could continue to rely on it for accessing accurate and consistent market data.