Wow, I am keeping an eye on this page.

1. **Problem Statement:

 - We Were trying AWS SDK for JavaScript (v3) to work with DynamoDB and trying to import the DynamoDB object along with the parse function from @aws-sdk/client-dynamodb.

2. **Proposed Solution:**tMandatory]

 - We were trying to use DynamoDB from aws-sdk but we didn't find that. So we decided to use '@aws-sdk/client-dynamodb'. And trying to use the parse function.

Code:

const { DynamoDB } = require('@aws-sdk/client-dynamodb'); 

const parse = DynamoDB.Converter.output;

3. **AI Integration Analysis:

 -Yes, AI can be used to get which function can be used here instead of parse. However, the parse function is not part of the DynamoDB object in the AWS SDK for JavaScript (v3).

AI Solution:

If you want to parse DynamoDB output in JavaScript, you should use the unmarshall function provided by @aws-sdk/util-dynamodb. Here's an example:

javascriptCopy codeconst { DynamoDB } = require('@aws-sdk/client-dynamodb');const { unmarshall } = require('@aws-sdk/util-dynamodb');

// Your DynamoDB response objectconst dynamoDBResponse = {
  Item: {
    // Your item properties here  },  // Other response properties here};
// Use unmarshall to parse DynamoDB outputconst parsedItem = unmarshall(dynamoDBResponse.Item);

console.log(parsedItem);
 

Make sure to replace // Your item properties here it with the actual properties of your DynamoDB item. The unmarshall function will convert the DynamoDB output format into a regular JavaScript object.

4. **Expected Impact:

 - AI models(Chat GPT) helped us to resolve this issue in a much faster way.

Hi I have submitted my response via email (Email send to  shiftsync@tricentis.com.)

I have submitted My response via Email, Thanks @sidharth shukla for this opportunity 

Below is the submission send via email:
 

Problem Statement:

The task was to design a crawler that can go through all the links in a web page to identify instances of "page not found" errors and perform functional verification The challenge was magnified by the need for this verification process to be done for 130 language specific web pages All links return a status code of 200 (indicating a successful response) and correctly display any "page not found" errors

However, there are several significant challenges with the existing use of the crawler. First, the number of visited links on many web pages places a heavy load on the server, resulting in potential performance issues and delayed authentication Second, the crawler generates a fake "page not found" error occurs on some links, even if manual verification confirms that these connections return a status code of 200. This difference caused errors in the verification process and destroys its effectiveness and reliability.

In summary, the problem statement:

1. Developing a crawler capable of efficiently traversing through a large volume of links across multiple webpages.
2. Ensuring accurate identification of "page not found" errors while minimizing false positives.
3. Mitigating the server load caused by the verification process to maintain performance and reliability.

Proposed Solution:

1. Developed a crawler which can handle large volumes of links across multiple webpages efficiently. Utilize asynchronous programming techniques and concurrency to parallelize the crawling process, minimizing the time and resources required for verification
   
   Cheerio library for HTML parsing.

fetch function from node-fetch module for making HTTP request

JSON file – country/language combination

delay - utility function to introduce delays in asynchronous operations.

Main Function: verify404
Function verifies if links on a website return a 404 error or not.

• Accepts a locator (CSS selector), a base URL,
• Process batches of URLs from the country and language list, checks each URL for broken links, and writes the results to files.
• Function handles error handling to deal with network errors and other exceptions.
• It writes both broken links and all links to separate text files.

Utility Function: processBatch

• This function processes a batch of URLs
• It fetches the HTML content of each URL, extracts links using the provided CSS selector, and checks each link for validity.
• It includes error handling for fetching HTML content and checking link validity

Utility Function: isLinkBroken

• This function checks if a given link returns a 404 error.
• It fetches the URL and checks the HTTP response status code.
• It introduces a delay between requests to avoid rate-limiting or overwhelming the server.

Execution Logic:

• The code iterates over an array of locator objects, each containing a CSS selector and output file name.
• It calls the verify404 function for each locator object, waits for a specified delay between iterations, and measures execution time.

Output:

• The script writes the results (both broken links and all links) to text files.
• It provides console logs for progress updates and error messages.

Overall, this script automates the process of checking for broken links on a website, handles network requests and parsing HTML, and writes the results to files for further analysis. It's designed to be flexible and configurable by providing options for specifying locators, delays, and output file names.

 

AI Integration Analysis:
 

AI-based web scraping techniques that can be used  to automatically locate and extract links from web pages. Algorithms could be used to analyze error messages returned by the server when a broken link is encountered.

Congratulations to Shal!

We're excited to announce @Shal as the winner of the AI Challenge by @sidharth shukla! The solution impressed us all with its exceptional quality and insightful analysis.

Our team and Sidharth reviewed all the submissions, and it wasn't an easy choice to define a winner - every submission was great. We sincerely thank all participants for their outstanding contributions!

Let's celebrate Shal's remarkable achievement together!