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Dergiler / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 4

God Class Detection Using Thresholds of Software Code Metrics Derived from Metaheuristic Optimization

Kapil Sharma(National Institute of Technology, Kurukshetra, Haryana, India)0009-0009-5016-6627
Jitender Kumar Chhabra(National Institute of Technology)0000-0002-2257-0982
Sayfa
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Yayın Tarihi
2026-09-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

These results highlight the importance of metaheuristic optimization for improved detection of God Class code smells.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The detection of God Class code smells is necessary for maintaining the quality, maintainability, and evolution of the software.
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Temel Bulgular & Veri:This study proposes a new methodology for God Class detection by calculating thresholds of software metrics (Chidamber and Kemerer (CK) metrics suite), WMC, RFC, CBO, LCOM, and LOC for more accurate detection of God Class smells.
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Sonuç & Katkı:These results highlight the importance of metaheuristic optimization for improved detection of God Class code smells.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Code smell · God Class · Software maintainability

Abstract

The detection of God Class code smells is necessary for maintaining the quality, maintainability, and evolution of the software. Traditional code smell detection methods often rely on static and developer intuition-based thresholds of the software metrics, which may not be applicable to projects of different sizes and domains. This study proposes a new methodology for God Class detection by calculating thresholds of software metrics (Chidamber and Kemerer (CK) metrics suite), WMC, RFC, CBO, LCOM, and LOC for more accurate detection of God Class smells. The optimal thresholds for these metrics were derived using four metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC), with the objective of maximizing the harmonic mean of sensitivity and specificity. Experimental findings compare the proposed approach with existing methods of threshold derivation, using evaluation parameters including accuracy, AUC, and the harmonic mean of sensitivity and specificity. Universal thresholds were also derived using weighted clustering for the software, where project-specific tuning is not possible. These results highlight the importance of metaheuristic optimization for improved detection of God Class code smells.

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Bu Makaleye Atıf Yap

Sharma, K., Chhabra, J. K. (2026). God Class Detection Using Thresholds of Software Code Metrics Derived from Metaheuristic Optimization. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 4. https://doi.org/10.35377/saucis...1797149

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