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Thinking Like a Software Engineer

Thinking Like a Software Engineer

Author: Devmountain Tutorials

Analyze the steps that the UX designer takes to make sure that the backend of the servers function effectively with the applications and systems and list some of the challenges for software engineers.

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what's covered
This section will explore a sample software engineering project by discussing:


Today is a big day at Poodle Jumper! Our team is preparing to release an iteration we’ve been working on for the past two months. The release provides a new feature for customers to schedule recurring services for their pets so they don’t have to schedule it manually every time.

This new feature required a few architectural changes, so we’ll need to bring the services down to make the changes. When this kind of deployment strategy is required, we do the work in the middle of the night to avoid interruption to our customers and providers.

We schedule it in advance and let the customers and support team know when they can expect the service to be back up and running. Before the release, I capture snapshots of the database and applications to make sure we have backups just in case something goes wrong.

Tonight, our release is scheduled for midnight. Everyone from the release team joins the virtual meeting from home. It starts by bringing down the service and updating the code and database to the new version. This can be as fast as five minutes or as slow as several hours, depending on the changes.

When the new version is in place, the automated tests run and the team splits up the manual test cases to make sure it is working as expected. I keep a really close eye on the error logs and analytics to capture anything unexpected.

If we find major issues that can’t be fixed quickly, the release is rolled back. Luckily, tonight the release is a success. We’ll get a few hours of sleep before logging in tomorrow to make sure everything is still working well. I’m on-call, so I know that if anything comes up, I’ll get a call.

The next morning, I come in and immediately look at the analytics and support queue. I know nothing serious happened because I didn’t get any alerts after the release, but I see one new issue the support team sent to us.

Amanda (QA) has taken the initiative to look at it and provided me with the details. When the user tries to schedule a recurring service for her dog, she sees an error message that says, “An unexpected error has occurred.” Amanda looked at the user's account and saw the same error when she tried to schedule as the user.

Being able to reproduce an error is extremely helpful for resolving it. We leveraged tools to debug the error and identified an issue with the data in the user’s account.

We call the environment users to interact with Production. It is bad practice to test with production data because it can negatively impact users. To avoid this, I make a clone the data and put it in a test environment. This will let us try the fix and verify it works. I run a database command to change the data for the user to the expected format. Amanda and I try to reproduce the problem, but the error message no longer displays, and the feature is working as expected.

I’m relieved to have figured out the problem for this one user, but I need to dig a little deeper. Is it possible that other users will experience this as well?

I run a query, or a search, in the database to identify if anyone else has the same issue with their data. The query returns 143 additional users in the bad state. I’m able to fix their account with the same command in the test environment. Amanda and I have a high level of confidence in the fix, so I execute the command in production and let the support team know we’ve resolved the issue.


It may sound like the job is done and it is time to move on, but I want to circle back and make sure we’ve put in mechanisms to prevent users from getting into this bad state in the future.

When a small number of users see an error, it is easy to blame them because the feature works for everyone else, but there is almost always an underlying issue that allows that error to happen. Preventing issues from happening and fixing them so that they stay fixed is important to the quality of our software.

Not all bugs or issues are solved so quickly. If we can’t reproduce the issue, it is hard to know what code to change and almost impossible to verify if it is resolved. There are times I find issues in our logs, and I have no idea how it happened.

It can be really frustrating when I don’t have enough information to fix something. That is why I’m grateful for our users who report errors that they see. We built a feature in the app so it is easy for users to send us issues and feedback. But we do our best to leverage analytics and monitoring, so users don’t have to tell us when our system breaks.

Every week I spend a portion of my time thinking about where things will break and creating fault-tolerant systems. Fault-tolerant means building something, so it won’t completely break when an error or something unexpected happens.

You’d be surprised at the weird things that can break an application. I’ve seen large organizations come to a screeching halt from a single typo. You may feel like you see technology outages all the time, and you’re right. They happen even to mature organizations. Computers are amazingly precise, and they can execute with amazing accuracy–when the humans programming them are equally precise.

At the end of the day, everyone on the development team is responsible for ensuring quality in our software. It takes a team effort to make this happen.


The learning opportunities and challenges of being a Software Engineer are vast. Our development team has been increasing our use of automation to speed up the release process for our product. This takes an investment from the company but will allow us to release changes with more reliability and speed while reducing costs.

CI/CD stands for 'continuous integration' and 'continuous delivery.' This includes a set of practices where code between developers is merged quickly or continuously integrated. Continuous delivery includes pipelines or automation to make the release process quick and easy.

As connection speeds increase and the cost to store data goes down, it is becoming easier to capture and store large amounts of data. Big data and machine learning have been buzz words for some time, but creating insights and value from that data is still a challenge for many organizations.

This challenge comes with an ethical dilemma; how do we give users the ability to know what data is collected and how it is used? In 2016, the European Parliament and Council created the General Data Protection Regulation (GDPR.)

This regulation was groundbreaking for the tech sector because it requires all companies that sell to European Union residents to comply regardless of the company’s location. Among other things, the regulation requires companies to gain consent from users to process their data, allow users to anonymize their data, and provide notifications to users in the event of a data breach.

GDPR has been the largest data regulation, but it isn’t the only one. The state of California adopted the California Consumer Privacy Act (CCPA) in 2018, which is similar to GDPR. With new and different regulations expected for other locations, the tech industry is coming together to establish best practices and tools to make compliance with privacy regulations easier.

Protecting Big Data

One of the most notorious challenges for Software Engineers is managing tech debt. Technical debt refers to an old or outdated code that needs to be reworked to improve the performance, maintainability, or security.

Maintaining code can quickly become a problem even at a small startup, when a simple website could have 10,000 lines of code. This grows quickly when you talk about large applications and operating systems that grow easily into millions of lines of code.

think about it
In 2017 Jeff Desjardins published an infographic showing how many million lines of code it takes to power different technologies. This estimates the Android operating system contains 12-15 million lines of code, but all of Google’s services are estimated at 2 billion lines of code. Which helps explain why they need so many engineers.

The amount of technical debt is increasing the need to focus on cybersecurity. Older versions of programming languages have known security vulnerabilities that can be exploited.

Keeping your programming languages up to date can require significant rework for engineers. It can feel like a constant battle between maintaining good hygiene on the code you already have while balancing the new development and needs of the organization.

Trying to stay up to speed with the changes can seem overwhelming, but our team has a culture of continuous learning. We set aside time every week to stay up on the new developments, and we share what we learn with the entire IT department during lunch 'n' learn meetings.