Cross-Project Software Effort Estimation Pendekatan Transfer Learning Menggunakan Machine Learning dan Optuna Hyperparameter Optimization

• RIAN HIDAYAT
• 14230027

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ABSTRAK

ABSTRAK

 

Nama : Rian Hidayat

NIM : 14230027

Program Studi : Ilmu Komputer

Fakultas : Teknologi Informasi

Jenjang : Strata Dua (S2)

Peminatan : Software Engineering & Data Science

Judul Tesis : Cross-Project Software Effort Estimation Pendekatan Transfer Learning Menggunakan Machine Learning dan Optuna Hyperparameter Optimization

 

Perkiraan upaya pengembangan perangkat lunak yang akurat tetap menjadi tantangan kritis dalam rekayasa perangkat lunak, terutama dalam skenario lintas perusahaan atau lintas dataset di mana heterogenitas data dalam fitur, skala, dan distribusi menurunkan kinerja model prediksi tradisional. Studi ini menyelidiki penerapan kerangka kerja Heterogeneous Transfer Learning (HTL) berbasis Deep Neural Network (DNN) untuk meningkatkan akurasi perkiraan upaya di seluruh dataset heterogen. Pendekatan yang diusulkan dimulai dengan identifikasi dan penyelarasan semantik fitur-fitur umum dari lima dataset yang tersedia secara publik: China, NASA93, Desharnais, Maxwell, dan SEERA. Pra-pemrosesan data meliputi transformasi Log1p untuk mengurangi kemiringan, pembersihan data, dan imputasi. Model DNN dasar dilatih pada dataset China dan dioptimalkan menggunakan Optuna untuk penyesuaian hiperparameter. Model yang dioptimalkan kemudian digunakan sebagai model sumber yang telah dilatih sebelumnya dalam eksperimen transfer learning, di mana berbagai strategi adaptasi dievaluasi dengan mengubah pembekuan dan pembukaan lapisan tersembunyi, mulai dari pembekuan penuh hingga penyesuaian penuh. Hasil eksperimen menunjukkan bahwa efektivitas kerangka kerja HTL sangat bergantung pada kesesuaian antara strategi adaptasi dan karakteristik dataset target. Peningkatan kinerja yang signifikan diamati pada NASA93 (R² meningkat dari 0,20 menjadi 0,56) dan SEERA (R² mencapai 0,9173 pada fine-tuning penuh). Dataset Maxwell menunjukkan kompatibilitas tinggi, mencapai R² 0,85 bahkan dengan representasi yang dibekukan. Sebaliknya, Desharnais tetap menantang, dengan nilai R² yang rendah secara konsisten (0,16), menunjukkan kesenjangan domain yang ekstrem. Temuan ini mengonfirmasi kelayakan transfer learning heterogen untuk estimasi upaya perangkat lunak lintas dataset dan menyoroti bahwa tidak ada strategi fine-tuning universal yang ada. Sebaliknya, kedalaman adaptasi harus disesuaikan dengan tingkat heterogenitas setiap domain target, memberikan wawasan praktis untuk mengembangkan model estimasi upaya yang lebih robust dan generalisable.

Kata kunci : Software Effort Estimation; Heterogeneous Transfer Learning; Deep Neural Networks; Cross-Company Prediction; Domain Adaptation; Hyperparameter Optimization Cross-Project Software Effort Estimation Pendekatan Transfer Learning Menggunakan Machine Learning dan Optuna Hyperparameter Optimization

KATA KUNCI

Cross project,Software Effort Estimation,machine learning

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

 

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