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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Syntax Literate: Jurnal Ilmiah Indonesia
Hadiningrum, Tiara Rahmania
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Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Relationship between features volatility and bug occurrence rate to support software evolution Hadiningrum, Tiara Rahmania; Mardiana, Bella Dwi; Rochimah, Siti
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5381-5389

Abstract

Software evolution is an essential foundation in delivering technology that adapts to user needs and industry dynamics. In an era of rapid technological development, software evolution is not just a necessity, but a must to ensure long-term relevance. Developers are faced with major challenges in maintaining and improving software quality over time. This research aims to investigate the correlation between feature volatility and bug occurrence rate in software evolution, to understand the impact of dynamic feature changes on software quality and development process. The research method uses commit analysis on the dataset as a marker of bug presence, studying the complex relationship between feature volatility and bug occurrence rate to reveal the interplay in software development. Validated datasets are measured by metrics and correlations are measured by Pearson-product-moment analysis. This research found a strong relationship between feature volatility and bug occurrence rate, suggesting that an increase in feature changes correlates with an increase in bugs that impact software stability and quality. This research provides important insights into the correlation between feature volatility and bug occurrence rates, guiding developers and quality practitioners to develop more effective testing strategies in dynamic development environments.
Comparative analysis of genetic algorithms for automated test case generation to support software quality Hadiningrum, Tiara Rahmania; Rochimah, Siti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp252-259

Abstract

Software testing is crucial for enhancing software quality, but designing test cases is a labor-intensive, resource-intensive, and time-consuming process. Additionally, test case designers often introduce subjectivity when creating test cases manually. To address these challenges, this paper compares three different approaches for automatically generating program branch coverage test cases: the parallel data generation algorithm (PDGA), a standard genetic algorithm (SGA), and a random test generation method. By leveraging genetic algorithms and parallel data generation techniques, these automated approaches aim to reduce the manual effort, resources, and potential biases involved in test case design, while improving the efficiency and effectiveness of achieving comprehensive branch coverage during software testing. The experimental results, conducted using five datasets with programs written in PHP, demonstrate that PDGA outperforms both SGA and random methods across various tested programs, achieving higher maximum and average coverage. Specifically, PDGA achieved an average coverage of 100% in the "calculator" program, highlighting its superior stability and efficiency. While SGA also shows good performance, it is not as optimal as PDGA, and the random method shows the lowest performance among the three. These findings underscore the potential of genetic algorithms, particularly PDGA, to enhance the coverage and quality of software testing, thereby significantly improving system reliability. 
Pengembangan Backend Aplikasi Pengenalan Plat Nomor Kendaraan Indonesia Hadiningrum, Tiara Rahmania; Suakanto, Sinung; Musnansyah, Ahmad
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i3.15411

Abstract

Penelitian ini bertujuan untuk mengembangkan sebuah Application Programming Interface (API) menggunakan framework Flask dalam sistem Automatic Number Plate Recognition (ANPR) untuk mengenali pelat nomor kendaraan secara otomatis. Pesatnya perkembangan teknologi pembelajaran mendalam yang memungkinkan inovasi dalam pengenalan pelat nomor kendaraan, yang memiliki berbagai aplikasi penting seperti penegakan hukum, manajemen parkir, pengaturan lalu lintas, dan lain-lain. Metode pengembangan API ini melibatkan pengujian terhadap 200 gambar, di mana 198 gambar berhasil mendeteksi pelat nomor dengan baik. Hasil penelitian ini adalah modul back end yang menyediakan fungsi lengkap bagi klien, memungkinkan mereka untuk menjalankan proses bisnis utama dengan efektif. Kesimpulannya, penggunaan framework Flask dalam mengembangkan API ANPR ini berhasil memberikan solusi yang dapat diandalkan dalam mengenali pelat nomor kendaraan secara otomatis dan efisien.
Co-Authors Ahmad Musnansyah Mardiana, Bella Dwi Sinung Suakanto Siti Rochimah