## 1 Introduction  
In the rapidly evolving digital landscape, software systems play a crucial role in automating various tasks on the Internet [1]. However, ensuring the reliability and efficiency of such systems remains a significant challenge. The emergence of errors or failures in their code can hinder their proper functioning, leading to service disruptions or incorrect results. To address this issue, a new generation of software has emerged that offers self-adaptive capabilities [2, 3, 4, 5] that can rectify errors and optimize performance automatically. In this article, we present an innovative software solution that not only executes pre-programmed tasks on the Internet but also has the ability to autonomously reconfigure itself in the face of code failures. As technology advances, the importance of robust and reliable software systems cannot be underestimated [6]. Organizations heavily rely on automated processes to optimize their operations, ranging from data collection and analysis to web scraping [7, 8, 9, 10] and information retrieval. Any disruption or malfunction in these critical processes can result in substantial financial losses, compromise data integrity, and diminish the user experience. The current state of research in the field of self-adaptive software systems [11, 12] underscores the need for innovative approaches to ensure the reliable and efficient execution of pre-programmed tasks. While traditional software often requires manual intervention for debugging and updates, the concept of self-adaptation promises to mitigate these challenges [13]. By integrating intelligent mechanisms such as fault detection, diagnosis, and recovery into the software architecture, self-adaptive systems can dynamically respond to emerging errors and autonomously adjust their behavior to ensure continuous functionality [14; 15]. Currently, we are in the era of automation for repetitive processes. Using artificial intelligence (AI) techniques contributes to saving time and resources that can be utilized for other purposes [16]. Within the field of AI, there are a number of commonly used learning methods, including neural networks [17] and expert systems. We will base our later discussion on the latter. Neural networks, as the name suggests, are designed to mimic the behavior of neurons, achieved by programming layers on demand. On the other hand, expert systems operate through pre-programmed logical reasoning [18], like a program that replicates the judgment of a human expert. Imagine for a moment that you need to extract information from a web page to store it in a database. Hiring people could be an option, but it would not be very cost-effective or efficient if you want something quick and economical. So, programming an 'expert system' with pre-programmed logic [19] to do it for you is a better choice. However, what if the web page changes some elements of its structure? Your software would no longer work, and you would have to reprogram it. This is where SiReTAAPP Version 1.0, created by [PERSON] and [PERSON], in Ameca, Jalisco. (Self-Adjusting Pre-Programmed Task Execution System) comes in. It is a web-based automated work software [20; 21; 22] that self-modifies based on changes or errors that have been pre-configured. It functions by having multiple processes working concurrently in parallel, monitoring the software. It utilizes a multi-master-single-slave communication architecture, where the slave, using the allowed processor cores, performs various tasks simultaneously, while the masters monitor the processes. In the event of a failure, the slave reports the error to the master modules. The master modules, with pre-programmed error information, resolve the conflict, rewrite the code of the slave module with the repaired error, and set it in motion again [23; 24; 25].