Analisis Efektivitas Regression Testing Menggunakan Metrik Kuantitatif pada Aplikasi Digital Banking: Studi Kasus BLU by BCA Digital
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Abstract
Penelitian ini menganalisis efektivitas regression testing menggunakan metrik kuantitatif pada aplikasi digital banking BLU by BCA Digital. Tujuannya adalah untuk mengevaluasi efektivitas strategi pengujian regresi dan mengidentifikasi area perbaikan dalam konteks aplikasi finansial yang kompleks. Metode yang digunakan adalah pendekatan kuantitatif eksplanatori dengan desain studi kasus tunggal. Data primer dikumpulkan secara prospektif dari artefak pengujian dan pipeline CI/CD selama tiga siklus rilis, mencakup 3.301 skenario pengujian. Analisis data menggunakan teknik statistik deskriptif dan inferensial, dengan fokus pada metrik kuantitatif seperti Defect Detection Percentage (DDP), Test Case Effectiveness (TCE), Requirements Coverage, dan Test Execution Efficiency. Hasil penelitian menunjukkan bahwa regression testing berjalan sangat efektif dengan penyelesaian 100% dan tingkat kegagalan fungsional hanya 0,06%. Strategi hybrid yang memadukan automation (32,87% cakupan) dan manual testing terbukti optimal; automation memberikan efisiensi dan stabilitas tinggi, sementara manual testing berperan krusial dalam mendeteksi defect kompleks. Namun, ditemukan disparitas kinerja antar modul dan tingginya skip rate automation (18,06%) sebagai area kritis. Kesimpulannya, pendekatan hybrid efektif menjamin kualitas rilis, namun diperlukan ekspansi cakupan otomasi dan perbaikan maintainability script. Penelitian ini berkontribusi pada pengembangan framework evaluasi kuantitatif untuk quality assurance di industri digital banking Indonesia.
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