Prediction Systems for Process Understandability and Software Metrics

Abstract

The abstract of this research study outlines the objective of validating prediction systems for process understandability and software metrics. In this study, we focus on assessing the accuracy and reliability of prediction systems that aim to provide insights into complex processes and software-related metrics. The process of validation involves defining clear objectives, gathering relevant data, preprocessing the data, performing feature engineering, selecting appropriate prediction models, and training and validating these models using cross-validation techniques. Additionally, we emphasize the importance of interpretability and explainability in the prediction process, which enables us to gain meaningful insights into the underlying processes. Furthermore, a comparative analysis is conducted to compare the predictions generated by the system with ground truth or expert judgments, thereby ensuring the accuracy and reliability of the predictions. The study adopts an iterative refinement approach to enhance the performance, interpretability, and usability of the prediction system based on feedback and validation results. By following this comprehensive validation process, we aim to establish reliable prediction systems that provide meaningful understandability of processes and software metrics.

Software metrics play a crucial role in assessing the quality, maintainability, and performance of software systems. However, understanding these metrics and their implications can be challenging, especially for non-technical stakeholders. This research study focuses on the understandability of software metrics and proposes a validation framework to assess the effectiveness of prediction systems in providing understandable insights.