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https://github.com/lucashomuniz/project-02

TRACKING USER ACCEPTANCE TESTING WITH POWERBI AND R
https://github.com/lucashomuniz/project-02

data-analysis data-visualization dax-languague powerbi-report powerbi-visuals powerquery r-language shiny-apps visuals

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TRACKING USER ACCEPTANCE TESTING WITH POWERBI AND R

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README 

        
# ✅ PROJECT-02



In this project, a comprehensive report was developed to monitor and track **User Acceptance Testing (UAT)** activities applied to a forecasting and demand-planning application. A company implemented a web-based **Shiny (R) Application** aimed at **centralizing and simplifying access to information** regarding products consumption and shipment forecasts. The methodology adopted prioritized clarity and practicality, using **interactive visuals** to highlight **key indicators** and testing stages. Complementarily, a **PowerBI Dashboard** enabled real-time tracking of the UAT process, providing complete visibility from initiation through to the conclusion of tests. Thus, the report not only facilitated **monitoring activities** but also played a crucial role in identifying, categorizing and addressing defects reported by users, ensuring **continuous improvement** before the application's final deployment.



**Keywords**: UAT, Business Analytics, Data Visualization, Data Analysis, R, Shinny, PowerBI, PowerQuery, DAX.



# ✅ PROCESS



The project began with approximately ten users responsible for conducting the acceptance tests on the **Shiny Application**. Each user was provided with individual files to systematically document identified defects. To optimize data management and centralize feedback, an **R script** was developed to **consolidate and organize the information** gathered from all users. This script performed two fundamental tasks: firstly, aggregating all individual defect reports, and secondly, automatically classifying these defects into two distinct categories by leveraging a **ChatGPT 4.5 API** integration. 



The **First Category** was segmented into four key areas: **Calculation Error**, **Visualization Issue**, **Format Modification** and **Mapping Alignment**. The **Second Category** condensed the reported issue into a concise description limited to **five words**, facilitating rapid identification. For example, a detailed issue initially described as *"Validating whether the waterfall chart accurately represents the volume. The chart should reflect the correct values based on backend data, and calculations must align with the defined aggregation rules. Currently, only some of the numbers match correctly"* would be categorized primarily as **Visualization Issue** and summarized as **"Waterfall Chart Volume Calculation Discrepancy."**



In addition to **Defect Categorization**, the **R script** also aggregated information about user progress through their respective **Test Cases**, verifying that all planned functionalities were thoroughly tested. This analysis allowed clear visibility into user engagement and the overall quality of the validation process. As outputs from this process, the script generated **two primary data files**: one containing the detailed **categorization of defects** and another tracking the **users' progress** through their assigned test activities. These files served as critical inputs for the final **PowerBI report**, which leveraged **DAX measures** and **interactive visualizations** to efficiently and intuitively track the entire **UAT process**.



# ✅ CONCLUSION



Ultimately, the report delivered was structured into two main sections. The first and second pages, provided a consolidated view of defects identified throughout the **UAT Master Defect Log**, clearly highlighting the total number of **defects found (247)**, classified into **Opened (41)**, **In-Progress (22)**, **Closed (184)** categories and also a **Total Percentage of Completion**. Interactive charts presented defect categorization percentages, revealing that most issues related to **Calculation Errors (59%)**, followed by **Mapping Alignment (23%)**, **Visualization Issues (13%)** and **Format Modifications (6%)**. This section also analyzed **Defect Criticality** clearly identifing the most active defects, facilitating **prioritization and corrective actions**.



![Screen Recording 2025-03-17 at 18 41 17](https://github.com/user-attachments/assets/de352d71-6475-461b-96e1-145c80d313a7)



The third page provided detailed insights into **User Test Progression** with their **Testing Activities**, organized into specific categories (**Functionality, Drivers, and Export Tests**). **DAX-based metrics** were employed to calculate real-time completion percentages, indicating an overall **Test Completion Rate of approximately 71.9%** indicating that the user are indeed developing the tests, with notable completion rates in **Functionality Test (73.3%)**, **Drivers Tests (71.43%)** and **Export Tests (71.94%)**. Furthermore, the report achieved its goal, making it possible to monitor, categorize and help resolve application defects, also made it easy to identify the users excelling in their testing activities, effectively assessing test efficacy and ensuring comprehensive application validation before final implementation.



![Screen Recording 2025-03-17 at 18 41 40](https://github.com/user-attachments/assets/9fb012b3-cd1b-4230-8f3b-82eab68f5748)



In conclusion, the resulting report delivered an efficient and intuitive platform for monitoring the entire UAT process. It significantly contributed to rapidly identifying issues and provided essential information to support developers in prioritizing and resolving defects. Consequently, the project not only enhanced the quality of the developed application but also substantially improved communication and collaboration between technical teams and end-users.



**Dashboard**: https://app.powerbi.com/view?r=eyJrIjoiZGEyYWFlMmUtMzA2OC00YjZmLTk0YTYtOGUyYjA5NWQ5YjdiIiwidCI6ImQ2OWE3NzgzLWU5MzctNDIzMi1iYTg1LTIwOTg0MDgzODJjOCJ9