## 3 Results
### Efficiency and Optimization  
Multiprocessing Testing with Variable Workloads: Tests were conducted with different workloads (C) to evaluate how the software leveraged multiprocessing. Workloads were varied to observe how the software adjusted to different levels of demand and its performance under heavy and light loads. This notation 'C' allows for an abstract and general representation of the operations performed in each iterative cycle of data loading and unloading, facilitating a clearer understanding of how the software adapts to different levels of demand in the tests. In cases where the task load is not specified, it is considered that one task equals 1 Mbps. A scenario was designed where incorrect data was deliberately injected into the software input process. This included providing variable values that did not match the specified requirements, such as negative values or text strings instead of numbers. The objective was to evaluate the software's ability to detect and correct these errors without interruptions in the process. A scenario was simulated where online traffic significantly increased, resulting in system overload. The software was subjected to a workload (W) much higher than normal, and its ability to maintain optimal performance and adapt to increased demand without downtime was evaluated. Tests were conducted where the structure of a target webpage experienced variations. For example, HTML tags were changed, and the order of elements on the page was modified. The software was evaluated for its ability to identify and adapt to these structural changes to complete the download task. Integrity errors were introduced into simulated download files, such as data corruption or incomplete files. The software was evaluated for its ability to verify the integrity of downloaded data and to correct or retry the download if any errors were detected. To achieve this, it is recommended to use expert systems and neural networks to continuously monitor and evaluate the software's performance. Error detection is based on identifying faults through automated diagnostics that compare current behavior with predefined patterns, allowing the system to react and autonomously correct faults [45; 49]. Tests were conducted on different operating systems and browsers to evaluate the software's compatibility and performance across a variety of user environments. A scenario was designed where the software's self-adaptation rate was progressively adjusted, from frequent adaptations to very rare adaptations. The objective was to evaluate how the software responded to changes in the self-adaptation rate and its ability to maintain stability and efficiency. Tests were conducted with different workloads (C) to evaluate how the software leveraged multiprocessing. Workloads were varied to observe how the software adjusted to different levels of demand and its performance under heavy and light loads. Deliberate code failures were introduced, such as division by zero, null pointers, or memory overflows. The software was evaluated for its ability to detect, report, and correct these errors without major system failures. A significantly large dataset was generated to evaluate the scalability of the software. The software was subjected to a workload involving the processing of large volumes of data, and its ability to adapt and maintain efficiency in high-demand environments was evaluated. Numerical methods and mathematical formulas were used for data approximation and model creation. Some of the most commonly used methods included:  
* Nonlinear regression method for modeling complex relationships between variables. This method is especially useful for fitting data to nonlinear models. * Principal Component Analysis (PCA) was employed to reduce the dimensionality of the data and explore patterns in multidimensional datasets. This method allows for the representation of complex data in a lower-dimensional space while preserving relevant information. * The wavelet transform was used to analyze data in the time and frequency domains.