Even Genspark embeds bugs: perfect code doesn't exist

Introduction

"Since Genspark wrote the code, it must be perfect."

Many people tend to think this way, but it's a big misconception. In the early stages of website development, I also blindly trusted and deployed AI-generated code. The result was... an application full of bugs.

Genspark certainly generates code quickly. However, like humans, AI also embeds bugs. This time, following the previous article, I will introduce AI-related bugs I actually encountered and how to deal with them.

Update: September 2026: Genspark's development features have changed considerably since this article was first published in 2025. What was previously called AI Developer is now Genspark Code, which Genspark describes as an autonomous coding agent capable of planning, coding, testing, and deploying applications. Even so, automatic testing does not mean AI-generated code is guaranteed to be bug-free. The basic lesson of this article—verify the code AI generates—still applies.

The Reality of AI Coding: The Bug Occurrence Rate is Surprisingly High

The original version of this article stated that "approximately 15-30% of AI-generated code contains some kind of issue."

I have removed that figure because I could not verify a reliable source that supports treating 15-30% as a general bug rate for all AI-generated code.

The actual rate varies significantly depending on the model, programming language, task, benchmark, and what is counted as a "problem."

What remains true is that AI-generated code can contain logic errors, security vulnerabilities, and performance problems.

Common AI-Related Bugs

1. Logic Errors

• Missing conditional branches
• Insufficient handling of edge cases
• Asynchronous processing conflicts

2. Security Issues

• Insufficient input validation
• SQL injection vulnerabilities
• Lacks of authentication/authorization

3. Performance Issues

• Redundant loop processing
• Inefficient database queries
• Memory leaks

Cursor also continues to develop its AI code-review tool, Bugbot. In 2026, Cursor added improvements that made Bugbot faster and allowed developers to run Bugbot and Security Review before pushing code.

The fact that AI development tools themselves include dedicated AI review systems is a good reminder that code generation and code verification are separate steps.

Real Experience 1: AI Generated an Infinite Loop

This happened when I was implementing a feature to cache user web function results on a website.

My Request: "Please create a function that skips recalculation if a cache exists."

Code Generated by Genspark

async function getCachedResult(userId: string) {
  let result = await cache.get(userId);

  while (!result) {
    result = await calculateCompatibility(userId);
    await cache.set(userId, result);
  }

  return result;
}

It looks fine at first glance, but there is a problem with this implementation.

The original version of this article stated that "if cache.get() fails, the while loop continues indefinitely" and that "even if cache saving fails, the loop cannot be exited."

Those explanations were not technically accurate.

If calculateCompatibility() returns a normal value, the loop exits after one iteration. And if cache.set() throws an exception, execution would normally stop rather than continue looping.

The real problem is simpler: a loop with no guaranteed termination condition was used for a task that only needed one conditional calculation.

Correct Implementation

async function getCachedResult(userId: string) {
  let result = await cache.get(userId);

  if (!result) {
    result = await calculateCompatibility(userId);
    await cache.set(userId, result);
  }

  return result;
}

Genspark understood the intention of "calculate if there is no cache," but it confused the while statement with the if statement.

Real Experience 2: Asynchronous Processing and Error Handling

I encountered another problem with Twitter API integration.

Code Generated by Genspark

async function postToTwitter(tweets: Tweet[]) {
  for (const tweet of tweets) {
    await api.post(tweet);
    console.log('Posted:', tweet.id);
  }
}

Issues

• Because await is used inside the loop, only one item can be posted at a time, making it slower.
• If one api.post() call throws an exception, the function stops at that point and the remaining tweets are not processed.
• There is no error handling or retry logic.

The original version of this article also stated that sequential await calls were more likely to hit API rate limits.

That is not necessarily true.

Sequential execution actually limits the number of simultaneous requests and can be safer for APIs with strict rate limits.

The original "improved version" used Promise.allSettled() to send every request in parallel. That can improve speed, but sending many requests simultaneously may actually make rate-limit problems worse.

So simply changing sequential processing to full parallel processing is not always the correct solution.

Improved Version

async function postToTwitter(tweets: Tweet[]) {
  const succeeded = [];
  const failed = [];

  for (const tweet of tweets) {
    try {
      await api.post(tweet);
      succeeded.push(tweet.id);
    } catch (error) {
      failed.push({
        id: tweet.id,
        error
      });
    }
  }

  console.log(`Posted: ${succeeded.length}, Failed: ${failed.length}`);
  return { succeeded, failed };
}

This version prioritizes reliability over speed: one failed request does not prevent the remaining tweets from being processed.

If faster processing is necessary, bounded concurrency can be used instead. The appropriate concurrency level, retry policy, and backoff strategy should be based on the actual API's rate-limit rules.

Genspark can generate code that appears to work, but whether that implementation is appropriate for the real API still needs to be checked.

Real Experience 3: Inefficient Database Queries

For the article list display feature, Genspark generated the following code:

Problematic Code

async function getArticlesWithCategories() {
  const articles = await db.query('SELECT * FROM articles');

  for (const article of articles) {
    article.category = await db.query(
      'SELECT name FROM categories WHERE id = ?',
      [article.category_id]
    );
  }

  return articles;
}

What's the Problem?

• N+1 Problem: If there are 100 articles, a total of 101 queries will be executed.
• Very high load on the database.
• Response time becomes extremely slow.

Correct Implementation (Using JOIN)

async function getArticlesWithCategories() {
  const articles = await db.query(`
    SELECT
      articles.*,
      categories.name as category_name
    FROM articles
    LEFT JOIN categories ON articles.category_id = categories.id
  `);

  return articles;
}

Genspark can generate basic SQL, but in this case, the generated code did not sufficiently consider performance optimization.

I later covered this problem in much more detail in "N+1 Problem: Common Bugs Introduced by Genspark and a Complete Solution".

Why Does Genspark Embed Bugs?

Main reasons why AI generates bugs:

1. Limitations in Contextual Understanding

• Important requirements may be missing from the prompt.
• The AI may not have enough information about the overall project structure.
• It may overlook consistency with existing code.

2. Edge Cases and Test Conditions Can Be Missed

• Code may focus only on the normal path.
• "null", empty data, failed network requests, and other exceptions may not be considered.
• Some problems only become visible when the code is actually executed.

3. Optimization and Security Are Not Automatically Guaranteed

• It can generate "working code" but not necessarily "good code."
• Performance and security still need to be checked.

The original version of this article stated that "approximately 20% of the code in the training data itself contains bugs" and that AI learns incorrect answers from Stack Overflow.

I have removed the numerical claim because I could not verify a sufficiently reliable source for using that percentage here.

In 2026, Genspark Code goes further than the development tools available when this article was written. Genspark describes it as an autonomous coding agent that can independently plan, code, test, and deploy.

This means it is easier to ask the AI not only to write code but also to run tests and fix problems.

However, if the test cases themselves are incomplete—or if the AI misunderstood the specification—it can still produce code that passes its own tests while doing the wrong thing.

Why Human Coding Knowledge is Important

So, should coding beginners give up on AI development? The answer is No.

The original version of this article stated: "However, a minimum level of coding knowledge is required."

I would phrase that a little more carefully today.

Genspark currently promotes Genspark Code as a tool that allows people to build complete applications without coding skills. There are also real-world examples of non-programmers building applications with AI.

So coding knowledge is no longer necessarily required to start building.

However, when you are creating software for production use, technical knowledge makes it much easier to recognize problems and judge whether the AI's solution is safe.

Useful Skills

1. Understanding Basic Syntax

• Variables, functions, conditional branches, loops
• Data types and how to read type errors

2. Debugging Basics

• How to read error messages
• Checking operation with console.log
• How to use breakpoints

3. Basic Algorithms

• Array manipulation
• Basics of asynchronous processing
• Fundamentals of database operations

With this knowledge, you can notice problems in the code generated by Genspark and instruct it to make corrections.

For more about the problems non-engineers tend to encounter with AI development, see "No-Code Development with Genspark AI Developer: 4 Pitfalls Non-Engineers Tend to Hit".

Practical Debugging Techniques

1. Habitual AI Code Review

Always check the following for code generated by Genspark:

• ✓ Is there error handling?
• ✓ Does it handle edge cases (null, empty arrays, etc.)?
• ✓ Is the loop's termination condition correct?
• ✓ Are there no asynchronous processing race conditions?
• ✓ Are database queries efficient?
• ✓ Are there any security issues?

After asking AI to fix code, don't rely only on a message saying "Fixed." Check what actually changed using Git diff or another comparison tool.

For production code, also see "Genspark Code Security Checklist: What to Verify Before Going Live".

2. Incremental Test Execution

Don't implement all features at once; test in small steps:

// Step 1: Test basic functionality
console.log('Test 1: Basic function');
const result1 = await basicFunction();
console.log('Result:', result1);

// Step 2: Test edge cases
console.log('Test 2: Empty input');
const result2 = await basicFunction([]);
console.log('Result:', result2);

// Step 3: Test error cases
console.log('Test 3: Invalid input');
try {
  const result3 = await basicFunction(null);
} catch (error) {
  console.log('Error caught:', error);
}

3. Ask AI to "Explain the Reason for Correction"

When requesting bug fixes:

❌ Bad example: "Fix this code."

✅ Good example: "An infinite loop is occurring in this code. Please identify the cause, provide a proposed fix, and explain the reason for the correction."

By asking for explanations, you can confirm the AI's understanding and prevent new bugs from being embedded.

4. Have Another AI Review the Code

This is something I use more often now than when I first wrote this article.

If you ask the same AI to review code it created itself, it may carry over the same assumptions and miss the same problems.

I now sometimes have code created with Claude Code reviewed by Codex and Antigravity.

In actual testing, the different AIs found different types of issues.

I wrote about that workflow in "I Had Codex and Antigravity Audit Code Built with Claude Code — Right Now, I Don't Think You Should Rely on Just One AI".

Cursor Bugbot and AI Debugging Tools

Cursor's Bugbot is an AI-powered code-review tool.

The original version of this article stated that Bugbot was released in August 2025 and linked to a third-party article. Rather than relying on that old description, it is more useful to look at what Bugbot can do today.

In June 2026, Cursor announced that Bugbot's average review time had fallen from about five minutes to about 90 seconds, while it found approximately 10% more bugs per review.

Cursor also added the ability to run Bugbot and Security Review with "/review" before pushing code.

Bugbot Features

• Detection of logical errors
• Scanning for security vulnerabilities
• Automated code review
• Pre-push review from Cursor

However, since Bugbot itself is also AI, 100% accuracy is not guaranteed. The final judgment must be made by humans.

For more about Bugbot, see "Cursor Bugbot and AI Debugging: What Developers Need to Know in 2025".

Summary: AI and Human Collaboration is Ideal

Genspark can greatly increase development speed, but skipping bug checks can cost far more time later.

Trust AI, but ensure thorough human oversight—this remains a good practice for AI development in 2026.

Next time, under the theme of "Genspark Freezes/Loops", I will introduce the issue of AI chat freezing and backup strategies to prevent data loss.