Application of the Gaussian Mixture Models (GMM) Algorithm to Identify Error Patterns in Compilation

Authors

  • Ayu Utari Nasution STIKOM Tunas Bangsa
  • Eko Prima Ambarita STIKOM Tunas Bangsa
  • Nurhidayanti Nurhidayanti STIKOM Tunas Bangsa
  • Heba Elsisy Fadlia STIKOM Tunas Bangsa
  • Victor Asido Elyakim P STIKOM Tunas Bangsa

DOI:

https://doi.org/10.55123/jomlai.v4i2.5960

Keywords:

Gaussian Mixture Models , Identify Error Patterns , Compilation

Abstract

In software development, the compilation process is a critical step that transforms source code into executable programs. These compilation errors can vary from simple syntax errors to more complex semantic errors, which often require a lot of time and effort to identify and fix. As a result, identifying recurring error patterns in the compilation process is important to improve software development efficiency. This study aims to explore the application of GMM in identifying error patterns in the compilation process. The results of this study indicate that the value of 0.58 on the Silhouette Score indicates that the clustering performed by GMM is quite good at identifying error patterns in compilation. The clusters are divided into 3, namely, Cluster 0 may indicate types of errors that occur more quickly (possibly related to syntax errors), with fewer lines of code and lower error frequency. Cluster 1 may represent more complex and less frequent errors (e.g., linker or runtime errors), with more lines of code. Cluster 2 may contain errors with different patterns, such as higher compilation duration or more frequent error frequency.

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Published

2025-06-20

How to Cite

Ayu Utari Nasution, Eko Prima Ambarita, Nurhidayanti, N., Heba Elsisy Fadlia, & Victor Asido Elyakim P. (2025). Application of the Gaussian Mixture Models (GMM) Algorithm to Identify Error Patterns in Compilation. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 4(2), 88–98. https://doi.org/10.55123/jomlai.v4i2.5960

Issue

Vol. 4 No. 2 (2025): Juni 2025

Section

Articles

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Copyright (c) 2025 Ayu Utari Nasution, Eko Prima Ambarita, Nurhidayanti Nurhidayanti, Heba Elsisy Fadlia, Victor Asido Elyakim P

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