## 2 Literature Review and Cost Estimation Methods
### Coestim--Design Simulator for Creating Construction Projects
Things such as response time under stressful conditions are considered in this step (see Table 2) along with transaction exposure per second (Tps). The usefulness of such a module can be tested especially in the case of data entered by loading them from files when later the data can be downloaded in a file with the same format and structure as the one from where they were taken, in which case the verification of data can be performed by comparing the original file with the downloaded one. Tests were also performed for the creating, modifying, deleting, list fetch, and fetch methods uniquely on an entity using 1000 to 10,000 clients executing 10 queries each to the server. We can say that database performance can be tested using Pgbench, the simple way to test PostgreSQL databases. It runs the same sequence of SQL commands repetitively, opening multiple sessions to the current databases, then calculates an average rate in the weight of transactions identified as transactions per second. In this case, test scenarios were outlined, taking into account cases where several more than 10, 100, 1000, or 10,000 clients were distributed over two execution threads, executing a different number of transactions for each client. These aspects are exposed in Figure 8 and presented in detail in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clients** & **Transactions Processed** & **Average Response Time** & **Tps (Excludes Database** **(ms)** & **Connection Time)** \\ \hline
1000 & 10,000 & 214 & 122.1 \\
2000 & 20,000 & 266 & 230.5 \\
3000 & 30,000 & 312 & 318.7 \\
4000 & 40,000 & 337 & 356.3 \\
5000 & 50,000 & 389 & 388.4 \\
6000 & 60,000 & 411 & 422.5 \\
7000 & 70,000 & 484 & 498.5 \\
8000 & 80,000 & 535 & 536.9 \\
9000 & 90,000 & 593 & 582.1 \\
10,000 & 100,000 & 688 & 619.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results regarding the testing of the database and the use of its performance standards regarding the number of connections. When we design a suite of testing and analysis tools to measure the performance of applications, along with that of software architectures, we must analyze how the database is populated with data, services, users, suppliers, materials, and their variations. Therefore, to more easily identify the information from the supplier, text-type columns are added for the \"SupplierItem\" entity, and this contains a link to the item from the supplier. In the case of the \"SupplierFeedOutModule\" entity, the \"CustomItemBaseMapping\" column is deleted because during implementation the \"ItemBaseMapping\" column proved to be sufficient for saving an item's mapping. Later, columns containing iterations for services and their identifying resources are added, asynchronously renaming price and quantity columns in real-time to generate quotes based on user queries. Renamed price and quantity columns changed date type to \"EstimateItem\" and \"ProjectExecutionItem\" to match their name and type from \"SupplierItemPrice\". The \"SuperServiceResource\" entity with the necessary columns was added to specify the number of hours required for a resource and to indicate whether it is on the entire super-service. Changes were also made to the data type for the amount column in the \"ServiceResource\" and \"EstimateServiceResource\" entities to accept actual values. What we can highlight, according to the scenarios tested in the case of Table 3, are aspects related to the number of clients that the Coestim platform can manage without compromising the integrity of the platform, and the graphic display in Figure 9. User sets exceeding 100,000 users executing over 1000,000 processed transactions are established. The flexibility of the platform and its adaptability were some of the main goals on which the entire architecture was coagulated. The development team's experience has been gained over the years from aspects regarding the reliability process of the existing platforms, especially during the autumn campaigns, when the e-commerce platforms are extremely overloaded and the waiting time increases.