Classifying Source Code Using Boosting Algorithms

Hundreds of lines of code make up large software systems. During maintenance and reuse, reading through the full source code will be quite tough. Many programmers stick to reading and navigating code than they develop it. Most experienced developers reuse existing code functionality. The cost of searching for and integrating reusable code determines the success of source code reuse. One method for identifying source code functionality names is to use a topic model. While numerous studies have focused on using topic models to analyze software systems, none have retrieved topics from actual source code syntax. By considering the optimal number of conditional statements, expressions, arrays, Boolean operators, and other unique programming keywords utilized is critical for any source code functionality name prediction. The dataset for the research model was collected from a variety of software developers' source code throughout the world. In software development, there are many different types of source code repositories, including source code algorithms. These repositories will be unstructured and unusable, requiring a technique to transform them into something useable. The source code data will be decomposed into smaller bits of programming language phrases using the Java parser library. Boosting algorithms are still more advantageous under the regime of limited training data and low knowledge for parameter modification. This study looked at supervised learning boosting algorithms such as XGBoost, CatBoost, LightGBM, and AdaBoost. It was discovered that the aforementioned algorithms performed equally, despite the fact that CatBoost surpassed all other methods based on accuracy with a value of 98%.