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Dogfooding 101: How Internal Beta Testing Can Help Developers

Dogfooding 101: How Internal Beta Testing Can Help Developers

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Kumari Trishya
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May 23, 2022
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3 min read
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Dogfooding. Hmm, maybe something my pet Labrador would love to chat about.
Thus thought I, when this topic first landed on my desk. Oh, how wrong I was!

The term ‘dogfooding’ has been in a coding lexicon since the late 1980s. It perhaps has its origin in the Alpo dog food commercials headed by actor Lorne Green. The phrase ‘eating your own dog food’ shows that one was proud enough of the products they built to use it on their own.

In the IT world, the use of the word ‘dogfooding’ can be traced back to Microsoft’s Paul Maritz who, in 1988, challenged the company’s employees to use the products they built. The idea behind it was very simple – if Microsoft’s own were not confident enough to use the software they spent hours making, why would someone else pay for it?

dogfooding - HackerEarth - best practices
Source: Google

So, what really is dogfooding?

More importantly, how is dogfooding different than the normal QA tests that every product has to go through? Let’s clarify!

While both alpha and beta testing phases are important in software development, dogfooding is slightly different than both. While both processes – dogfooding and testing – involve using the software to see how it holds up in different use cases, the focus during alpha and beta testing is on finding code bugs and errors. Dogfooding, too, can help dig up errors in the product; however, the main focus here is to interact with the software as a user and find how the developers can improve upon the base infrastructure.

Let’s take an example here.
Say you were testing out an email client. During the QA phase you would look at how the client performs when handling bulk emails; if the spam filter is working well; if image-heavy newsletters are rendering well. These are known use cases that every quality check would include.
During the dogfooding phase, you spend days, weeks, months using the product. You are not actively looking for bugs, but trying to sense if the product you built is easier to use versus other email clients you have used in the past. Is the UI/UX easy to navigate? Are the features useful? Most importantly, how much do you itch to go back to your previous email sender? If you itch for a change, most probably your prospective users will, too.

How do you integrate dogfooding in your testing strategy?

Dogfooding can be used as a complementary and cost-effective testing strategy that works in tandem with your established alpha and beta testing process. Dogfooding can in fact, be used synonymously with internal beta testing.

Letting the internal team test out a new product before the end users is beneficial for two reasons:

  • External users who come across major difficulties or bugs in your product will most likely stop using it. This effect is easily counteracted by dogfooding.
  • Second, you will not only spare the customer support team a lot of heartache, but also help them prepare better for user queries.
Dogfooding - Apple
Source: Google

What makes dogfooding a great beta testing strategy?

Google, that much-loved tech giant loves dogfooding. Here’s what they say about using dogfooding and beta testing on the Google Testing Blog: “We have a large ecosystem of development / office tools and use them for nearly everything we do. Because we use them on a daily basis, we can dogfood releases company-wide before launching to the public.

When a name like Google stands behind a process, you know it has some merit. Here are some of the benefits of dogfooding during standard testing:

1. Allows you to scale quality testing environments with real-time feedback: Dogfooding helps developers flag the flaws in their original design. We know that it is very hard to exhaust a product in terms of testing – it takes months; and the whole process repeats itself when you add new features. When developers ‘eat their own dog food’, it helps widen the QA pool and look for nuances no one would have thought of.

2. No risk of losing customers: A lot of the time, businesses are under tremendous pressure to release a product. While timelines need to be adhered to, they cannot come at the cost of quality. By turning your company employees into users, you ensure that quality is maintained and no customer is irked or lost in the process.

3. Cuts down on development and support costs: What’s easier – shipping a half-baked product and then hiring a huge support staff to field customer calls and queries, or spending time and effort testing the product and then asking customers to use it? By using dogfooding as a beta testing standard, you can cut down on all these extra overheads.

4. Creates a collaborative environment and breaks down departmental silos: When every employee in the org uses their products, they have a better understanding of how things work. It will help the product team close the distance between them and the end users, and prepare your CSM team to handle incoming requests.

While these are some good reasons to use dogfooding, there may be instances in which it may not be a good fit for your company. A couple of them come to mind:
Scenario 1: When you have created a specialized software
If you create a software for a very niche target audience, then it may not be a good idea to have your team use it and send feedback. Let’s say you have a product aimed at doctors, then you cannot have someone who has no knowledge of medicine using the product and testing it.
Scenario 2: When your product is still in an immature stage
Dogfooding should be done only when the product enters a certain stage of development. If you have just created an MVP, asking your entire team to test it may not be a good idea. While you may receive a ton of feedback, it might end up creating obstacles in product development instead of speeding things up.

How to roll out a dogfooding program?

The first step is to create a product that users are going to love. In the world of SaaS, they say that you should create products that “solve your own problem”. Once you have this figured out, the rest is a 4-step process that I will list out below:

1. Secure the right buy-ins: Dogfooding is an org-wide exercise and it is important that everyone knows why it is necessary, and what are the expectations from them. Folks in non-technical functions like marketing or sales may not always know how to spot or report an error. A small crash course will help tons! Moreover, it will prevent your teammates feeling like they have been forced to do QA on behalf of the product/engineering team, and give up the right to use products and platforms they actually would on a daily basis.

2. Segment the testers: Different user segments would have different requirements from the same product. It is a good idea to segment your colleagues into buckets so you know what kind of requests are coming from the CXO-level users, and what are the main pain points of a mid-level manager. You can then choose to address these issues based on your internal priority list.

3. Set up a feedback mechanism: Going back to point #1, not every person in your company is a QA or an engineer, or even familiar with the process of reporting bugs, or raising a feature request. Create a feedback mechanism which is simple to use and help your co-workers familiarize themselves with it before you ask them to test the product.

4. Incentivize the process: Last but not the least, have fun with it! Gamification is a proven way to get people involved in serious tasks like hunting bugs 🙂 So, add incentives and giveaways for colleagues who report major issues, and whose feedback helps in improving the product.

5. Account for bias: At the end of the day, your colleagues and co-workers may have a bias towards the product because it’s their handiwork. Don’t forget to factor that in when you look at the feedback. In this case, your harshest critic is your best friend!

Eat the dog food, but don’t make it dinner!

I hope this piece has helped you understand the dos and don’ts of dogfooding during product development. Remember this though – dogfooding is not an alternative to alpha and beta testing, neither should it be the sole bulwark supporting your product development cycle.

Use it wisely and it can make a world of difference to your engineering teams. Now back to feeding the Labrador, I go!

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Author
Kumari Trishya
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May 23, 2022
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3 min read
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Vibe Coding: Shaping the Future of Software

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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