Optimalisasi Automated Unit Testing pada Pemrograman Berbasis Web Menggunakan Pendekatan Reinforcement Learning
Muh Akram Riyadi Ramadhan(1*); Muhammad Faisal(2); Lukman Lukman(3); Desy Anggreani(4); M. Agusalim(5); Irnawaty Idrus(6); Soemitro Emin Praja(7);
(1) Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(2) Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(3) Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(4) Department of Informatics, Universitas Muhammadiyah Makassar, Makassar, Indonesia
(5) Department of Architecture, Universitas Muhammadiyah Makassar, Indonesia
(6) Department of Water Resources Engineering, Universitas Muhammadiyah Makassar, Indonesia
(7) Department of Urban and Regional Planning, Universitas Muhammadiyah Makassar, Indonesia
(*) Corresponding Author
AbstractPengujian perangkat lunak penting dalam pengembangan aplikasi web, namun automated unit testing konvensional berbasis aturan statis terbatas menghadapi kompleksitas sistem modern. Penelitian ini memformulasikan prioritisasi kasus uji sebagai Markov Decision Process: agen Reinforcement Learning mengamati state, memilih kasus uji, dan menerima reward multiobjektif yang memadukan cakupan kode dan deteksi kesalahan dikurangi biaya eksekusi. Tiga algoritma (DQN, DDQN, PPO) dibandingkan terhadap baseline Random dan Greedy menggunakan lima seed dan uji Mann–Whitney U. Pada System Under Test berbasis Flask, DDQN memperoleh performa terbaik dengan APFD 0,8504, NAPFD 0,8447, dan F1 0,4183, mengungguli baseline secara signifikan (nilai p terkecil 7,15 × 10⁻¹⁴⁴) dengan latensi keputusan 3,0033 ms, kurang dari separuh PPO. Greedy mencapai cakupan tertinggi namun deteksi terendah, menegaskan cakupan bukan indikator memadai. Validasi eksternal pada BugsInPy (82 bug web) mengonfirmasi arah keunggulan serupa dengan margin lebih kecil. Reinforcement Learning merupakan pendekatan menjanjikan dan dapat direproduksi untuk automated unit testing adaptif pada aplikasi web.
KeywordsReinforcement Learning; Pengujian Unit Otomatis; Pengujian Perangkat Lunak; Pengujian Adaptif; Pemrograman Berbasis Web
|
Article MetricsAbstract view: 54 times |
Digital Object Identifier https://doi.org/10.33096/busiti.v7i3.3507
|
Cite |
References
T. Chojnacki and L. Madeyski, “Test case prioritization: A systematic review using snowballing and TCPFramework with approach combinators,” Inf. Softw. Technol., vol. 194, p. 108062, 2026, doi: 10.1016/j.infsof.2026.108062.
Q. Zhang, C. Fang, W. Sun, S. Yu, Y. Xu, and Y. Liu, “Test case prioritization using partial attention,” J. Syst. Softw., vol. 192, p. 111419, 2022, doi: 10.1016/j.jss.2022.111419.
J. A. Silveira, L. Vieira, and N. Ferreira, “TPVis: A visual analytics system for exploring test case prioritization methods,” Comput. & Graph., vol. 124, p. 104064, 2024, doi: 10.1016/j.cag.2024.104064.
M. Faisal et al., “Assessing AI-Driven Personalization in Smart Cities Using Hybrid Machine Learning and MCDM Approach,” HighTech Innov. J., vol. 6, no. 3, pp. 770–792, Sep. 2025, doi: 10.28991/HIJ-2025-06-03-03.
F. Pagano, M. Ceccato, A. Merlo, and P. Tonella, “Multi-agent deep reinforcement learning for penetration testing of IoT devices through their mobile companion app,” J. Syst. Softw., vol. 239, p. 112918, 2026, doi: 10.1016/j.jss.2026.112918.
A. Abo-eleneen, A. Palliyali, and C. Catal, “The role of Reinforcement Learning in software testing,” Inf. Softw. Technol., vol. 164, p. 107325, 2023, doi: 10.1016/j.infsof.2023.107325.
A. P. Marugán, “Applications of Reinforcement Learning for maintenance of engineering systems: A review,” Adv. Eng. Softw., vol. 183, p. 103487, 2023, doi: 10.1016/j.advengsoft.2023.103487.
A. Seyyedabbasi, “A reinforcement learning-based metaheuristic algorithm for solving global optimization problems,” Adv. Eng. Softw., vol. 178, p. 103411, 2023, doi: 10.1016/j.advengsoft.2023.103411.
W. Takerngsaksiri, R. Charakorn, C. Tantithamthavorn, and Y.-F. Li, “Pytester: Deep reinforcement learning for text-to-testcase generation,” J. Syst. Softw., vol. 224, p. 112381, 2025, doi: 10.1016/j.jss.2025.112381.
M. R. Haris et al., “Leveraging a Hybrid Fuzzy C-Means-PCA Model for Identifying Speech Therapy Needs in Children,” J. Data Sci. Intell. Syst., Jun. 2026, doi: 10.47852/bonviewJDSIS62028311.
P. Garrad and S. Unnikrishnan, “Reinforcement learning in VANET penetration testing,” Results Eng., vol. 17, p. 100970, 2023, doi: 10.1016/j.rineng.2023.100970.
Y. Ding, Y. Zhang, G. Yuan, S. Jiang, W. Dai, and Y. Zhang, “Integration test order generation based on reinforcement learning considering class importance,” J. Syst. Softw., vol. 205, p. 111823, 2023, doi: 10.1016/j.jss.2023.111823.
Z. Qian, Q. Yu, H. Zhu, J. Liu, and T. Fu, “Reinforcement learning for test case prioritization based on LLEed K-means clustering and dynamic priority factor,” Inf. Softw. Technol., vol. 179, p. 107654, 2025, doi: 10.1016/j.infsof.2024.107654.
T. Shu, C. Wu, and Z. Ding, “Boosting input data sequences generation for testing EFSM-specified systems using deep reinforcement learning,” Inf. Softw. Technol., vol. 155, p. 107114, 2023, doi: 10.1016/j.infsof.2022.107114.
X. Wang and S. Zhang, “Cluster-based adaptive test case prioritization,” Inf. Softw. Technol., vol. 165, p. 107339, 2024, doi: 10.1016/j.infsof.2023.107339.
E. A. da Roza, J. A. do Prado Lima, and S. R. Vergilio, “On the use of contextual information for machine learning based test case prioritization in continuous integration development,” Inf. Softw. Technol., vol. 171, p. 107444, 2024, doi: 10.1016/j.infsof.2024.107444.
W. D. F. Mendonça, W. K. G. Assunção, and S. R. Vergilio, “Feature-oriented test case selection and prioritization during the evolution of highly-configurable systems,” J. Syst. Softw., vol. 217, p. 112157, 2024, doi: 10.1016/j.jss.2024.112157.
A. Alwadaeen and Z. Alzamil, “Optimized greedy algorithm for efficient test case prioritization,” Sci. Comput. Program., vol. 253, p. 103489, 2026, doi: 10.1016/j.scico.2026.103489.
S. Alagarsamy, C. Tantithamthavorn, and A. Aleti, “A3Test: Assertion-Augmented Automated Test case generation,” Inf. Softw. Technol., vol. 176, p. 107565, 2024, doi: 10.1016/j.infsof.2024.107565.
Y. Shi, B. Yin, and J.-A. Shi, “Markov model based coverage testing of deep learning software systems,” Inf. Softw. Technol., vol. 179, p. 107628, 2025, doi: 10.1016/j.infsof.2024.107628.
M. Waltz and O. Okhrin, “Addressing maximization bias in reinforcement learning with two-sample testing,” Artif. Intell., vol. 336, p. 104204, 2024, doi: 10.1016/j.artint.2024.104204.
L. Chavali, A. Krishnan, P. Saxena, B. Mitra, and A. Sreevallabh Chivukula, “Off-policy actor-critic deep reinforcement learning methods for alert prioritization in intrusion detection systems,” Comput. & Secur., vol. 142, p. 103854, 2024, doi: 10.1016/j.cose.2024.103854.
N. Cardozo and I. Dusparic, “Auto-COP: Adaptation generation in Context-oriented Programming using Reinforcement Learning options,” Inf. Softw. Technol., vol. 164, p. 107308, 2023, doi: 10.1016/j.infsof.2023.107308.
M. Zhao, R. Hou, H. Li, and M. Ren, “A hybrid grey wolf optimizer using opposition-based learning, sine cosine algorithm and reinforcement learning for reliable scheduling and resource allocation,” J. Syst. Softw., vol. 205, p. 111801, 2023, doi: 10.1016/j.jss.2023.111801.
A. Sunba, J. Hassine, and M. Ahmed, “Testing reinforcement learning systems: A comprehensive review,” J. Syst. Softw., vol. 231, p. 112563, 2026, doi: 10.1016/j.jss.2025.112563.
T. K. A. Choudhury, M. Behera, S. K. Dash, S. K. Pani, and J. Mishra, “AnoLSTM—A Deep Learning Approach for Test Cases Prioritization,” in Procedia Computer Science, Elsevier BV, 2025, pp. 1793–1803. doi: 10.1016/j.procs.2025.04.431.
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Buletin Sistem Informasi dan Teknologi Islam (BUSITI)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.