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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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Kumari Trishya
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May 23, 2022
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3 min read
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How I used VibeCode Arena platform to build code using AI and leant how to improve it

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

  • Clean, semantic HTML
  • Smooth CSS transitions
  • Keyboard navigation support
  • ARIA labels for accessibility
  • Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

  • No DNS-prefetch hints for external image domains
  • Missing width/height attributes causing layout shift
  • No preload directives for critical resources
  • Missing CSS containment properties
  • No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

  • No 'use strict' directive in JavaScript
  • Missing package.json for dependency management
  • No test files
  • Missing README documentation
  • No .gitignore or version control setup
  • Could use functional array methods for cleaner code
  • Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

  • Security vulnerabilities
  • Performance/Efficiency issues
  • Performance optimization opportunities
  • Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

  • What actually improved (and by how much)
  • What new issues I might have introduced
  • Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

  • How to implement Content Security Policy correctly
  • Why DOM depth matters for rendering performance
  • What CSS containment does and when to use it
  • How to structure code for better maintainability
  • Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

The Mobile Dev Hiring Landscape Just Changed

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Article content

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Article content

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin
This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way
Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

For Recruiters & Hiring Managers:

  • Accurately assess real-world development skills.
  • Gain deeper insights into candidate proficiency.
  • Hire with greater confidence and speed.
  • Reduce candidate drop-off from technical friction.

For Candidates:

  • Enjoy a seamless, efficient assessment experience.
  • No need to switch between different tools or manage complex setups.
  • Focus purely on showcasing skills, not environment configurations.
  • Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

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

Vibe Coding Difference

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 or Hostinger Horizons 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.

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