## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Files with the Results of Our Work** & **Who Use the File** & **Description** \\ \hline trained\_error\_prediction\_model\_h5 & \
\begin{tabular}{c} Neuronal network trained without \\ reinforcement algorithm \\ \end{tabular} &
\begin{tabular}{c} Model trained without reinforcement \\ learning algorithm. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._to production. Additionally, the integration of automatic rollback mechanisms and constant monitoring ensures that any introduced error can be quickly identified and corrected without affecting system stability. A/B testing is an experimental method used to compare two versions of a variable to determine which is more effective. In the context of our study, these tests are used to evaluate and optimize the performance of self-adaptive software in different workload scenarios. In an A/B test, users or traffic are divided into two groups: Group A, which experiences the original (or control) version of the software, and Group B, which tests a modified (or variant) version. The objective of A/B testing is to identify which of the two versions offers better performance in terms of specific metrics, such as response time, processing efficiency, and fault tolerance. This approach allows for incremental, data-driven adjustments, continuously improving the quality and performance of the software. Self-rewriting significantly enhances system performance and efficiency by enabling autonomous reconfiguration in the face of errors, thus reducing downtime and improving system responsiveness. Our results demonstrate an improvement in the self-adaptation rate and faster response times due to the use of multiprocessing and automated optimization [52; 53]. Our experiments show that self-rewriting not only enhances system resilience but also its overall performance. By implementing multiprocessing techniques and automated optimization, we observed a significant reduction in downtime and an improvement in response speed under variable loads, thus validating the effectiveness of self-adaptive systems in high-demand scenarios. To ensure the coherence and integrity of data in self-rewriting software, it should employ a robust architecture that includes: (1) continuous monitoring: constant supervision of processes and data; (2) predefined abstractions: Utilization of pre-programmed steps to address errors; and (3) error handling: storage of errors with detailed descriptions and the ability for automated resolution based on previous diagnostics [54; 55]. In our research, in addition to what [PERSON] and [PERSON] suggest, we developed and tested a (4) version control system that allows the software to make changes without compromising data integrity. This system includes an automatic rollback feature that is activated when inconsistencies are detected, ensuring that the data remains coherent and accurate throughout the rewriting process. Although the software we propose has a self-adaptation capability with an 80% success rate to errors and changes in its environment, there are still complex situations where the adaptation may not be completely effective. These may include drastic changes in web page structures or the introduction of new types of errors that were not considered in the initial models. The use of virtual cables and multiprocessing significantly improved the speed and efficiency of the software, but the implementation and configuration of these technologies can increase system complexity, which could limit its scalability in certain operational environments. Our proposal uses multiprocessing to improve performance, but this also implies intensive use of system resources. A robust hardware configuration is required to fully exploit the software's capabilities, which may not be available on all platforms. Despite the fact that the use of multiprocessing and virtual cables improves scalability, there is still an inherent limitation in terms of parallelism and the efficient handling of large volumes of data.