PENINGKATAN KUALITAS PERANGKAT LUNAK DENGAN REGRESSION TESTING BERBASIS LEAN PADA APLIKASI CLIENT-SERVER

Puguh Jayadi; Renny Sari Dewi; Sri Anardani

doi:10.46576/djtechno.v6i1.6040

PENINGKATAN KUALITAS PERANGKAT LUNAK DENGAN REGRESSION TESTING BERBASIS LEAN PADA APLIKASI CLIENT-SERVER

Puguh Jayadi, Renny Sari Dewi, Sri Anardani

Abstract

Pengujian perangkat lunak sejak dulu memberikan kontribusi signifikan terhadap kualitas perangkat lunak yang dikembangkan. Terdapat beberapa pendekatan dalam melakukan pengujian perangkat lunak seperti Regression testing. Namun di sisi lain, Regression testing seringkali memakan waktu dan sumber daya besar, terutama pada aplikasi dengan interaksi kompleks antara client dan server. Untuk mengatasi masalah tersebut, penelitian ini mengusulkan integrasi prinsip Lean, yang berfokus pada penghapusan aktivitas non-nilai tambah (waste) dan optimalisasi alur kerja pengujian. Metode ini diimplementasikan secara iteratif, dimulai dari analisis kebutuhan, perancangan test case, pelaksanaan pengujian, identifikasi bug, hingga evaluasi hasil. Eksperimen dilakukan pada aplikasi impor data Excel-MySQL, dengan membandingkan waktu pengujian sebelum dan setelah penerapan Lean. Hasil penelitian menunjukkan pengurangan waktu regression testing hingga 64,28% (dari 14 jam menjadi 9 jam), dengan 94,1% test case (16 dari 17) berhasil sesuai ekspektasi. Temuan ini membuktikan bahwa Lean Development efektif dalam meningkatkan efisiensi tanpa mengorbankan cakupan pengujian. Penelitian ini memberikan kontribusi praktis bagi pengembang aplikasi client-server skala kecil, dengan menawarkan kerangka kerja yang adaptif dan hemat sumber daya.

Keywords

Pengujian Perangkat Lunak, Regression Testing, Lean Development, Aplikasi Client-Server, Kualitas Perangkat Lunak

Full Text:

PDF

References

M. A. Jamil and M. K. Nour, “Managing Software Testing Technical Debt Using Evolutionary Algorithms,” Computers, Materials and Continua, vol. 73, no. 1, pp. 735–747, 2022, doi: https://doi.org/10.32604/cmc.2022.028386.

F. Dobslaw, R. Wan, and Y. Hao, “Generic and industrial scale many-criteria regression test selection,” Journal of Systems and Software, vol. 205, p. 111802, 2023, doi: https://doi.org/10.1016/j.jss.2023.111802.

R. Sulaiman, D. A. Jawawi, and S. Halim, “Cost-effective test case generation with the hyper-heuristic for software product line testing,” Advances in Engineering Software, vol. 175, p. 103335, 2023, doi: https://doi.org/10.1016/j.advengsoft.2022.103335.

M. Kessel and C. Atkinson, “Promoting open science in test-driven software experiments,” Journal of Systems and Software, vol. 212, p. 111971, 2024, doi: https://doi.org/10.1016/j.jss.2024.111971.

F. S. Ahmed, A. Majeed, and T. A. Khan, “Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms,” Computers, Materials and Continua, vol. 74, no. 1, pp. 2211–2238, 2022, doi: https://doi.org/10.32604/cmc.2023.032664.

K. Patel, R. M. Hierons, and D. Clark, “An information theoretic notion of software testability,” Inf Softw Technol, vol. 143, p. 106759, 2022, doi: https://doi.org/10.1016/j.infsof.2021.106759.

L. Leoni, F. De Carlo, M. M. Abaei, and A. BahooToroody, “A hierarchical Bayesian regression framework for enabling online reliability estimation and condition-based maintenance through accelerated testing,” Comput Ind, vol. 139, p. 103645, 2022, doi: https://doi.org/10.1016/j.compind.2022.103645.

W. Messner, “From black box to clear box: A hypothesis testing framework for scalar regression problems using deep artificial neural networks,” Appl Soft Comput, vol. 146, p. 110729, 2023, doi: https://doi.org/10.1016/j.asoc.2023.110729.

T. König and M. Kley, “A software framework for virtual testing of a production control system,” Procedia Comput Sci, vol. 225, pp. 1495–1503, 2023, doi: https://doi.org/10.1016/j.procs.2023.10.138.

R. Sheikh, M. I. Babar, R. Butt, A. Abdelmaboud, and T. A. Elfadil Eisa, “An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing,” Computers, Materials and Continua, vol. 74, no. 3, pp. 6789–6806, 2022, doi: https://doi.org/10.32604/cmc.2023.028625.

M. K. Shin, S. Ghosh, and L. R. Vijayasarathy, “An empirical comparison of four Java-based regression test selection techniques,” Journal of Systems and Software, vol. 186, p. 111174, 2022, doi: https://doi.org/10.1016/j.jss.2021.111174.

Y. Zhang, “Application and Practice of Intelligent Algorithms in Computer Software Design and Testing,” Procedia Comput Sci, vol. 243, pp. 708–715, 2024, doi: https://doi.org/10.1016/j.procs.2024.09.085.

P. Jayadi, “Lean Development pada Efisiensi Pengembangan Aplikasi Client-Server untuk Import Data yang Dinamis,” Jurnal Manajemen Sistem Informasi (JMASIF), vol. 2, no. 2, pp. 47–55, 2023, doi: 10.59431/jmasif.v2i2.395.

I. Ilays et al., “Towards Improving the Quality of Requirement and Testing Process in Agile Software Development: An Empirical Study,” Computers, Materials and Continua, vol. 80, no. 3, pp. 3761–3784, 2024, doi: https://doi.org/10.32604/cmc.2024.053830.

S. Najihi, S. Elhadi, R. A. Abdelouahid, and A. Marzak, “Software Testing from an Agile and Traditional view,” Procedia Comput Sci, vol. 203, pp. 775–782, 2022, doi: https://doi.org/10.1016/j.procs.2022.07.116.

N. M. Minhas, J. Börstler, and K. Petersen, “Checklists to support decision-making in regression testing,” Journal of Systems and Software, vol. 202, p. 111697, 2023, doi: https://doi.org/10.1016/j.jss.2023.111697.

M. Waseem, P. Liang, M. Shahin, A. Di Salle, and G. Márquez, “Design, monitoring, and testing of microservices systems: The practitioners’ perspective,” Journal of Systems and Software, vol. 182, p. 111061, 2021, doi: https://doi.org/10.1016/j.jss.2021.111061.

T. Mårtensson, D. Ståhl, A. Martini, and J. Bosch, “Efficient and effective exploratory testing of large-scale software systems,” Journal of Systems and Software, vol. 174, p. 110890, 2021, doi: https://doi.org/10.1016/j.jss.2020.110890.

A. M. Kazerouni, J. C. Davis, A. Basak, C. A. Shaffer, F. Servant, and S. H. Edwards, “Fast and accurate incremental feedback for students’ software tests using selective mutation analysis,” Journal of Systems and Software, vol. 175, p. 110905, 2021, doi: https://doi.org/10.1016/j.jss.2021.110905.

M. Hamidi, M. Adelzadeh, and H. Hajiloo, “Improving stability in hybrid fire testing: Advancements in analysis method and software implementation,” Comput Struct, vol. 302, p. 107473, 2024, doi: https://doi.org/10.1016/j.compstruc.2024.107473.

V. Casola, A. De Benedictis, C. Mazzocca, and V. Orbinato, “Secure software development and testing: A model-based methodology,” Comput Secur, vol. 137, p. 103639, 2024, doi: https://doi.org/10.1016/j.cose.2023.103639.

M. Zakeri-Nasrabadi, S. Parsa, and S. Jafari, “Measuring and improving software testability at the design level,” Inf Softw Technol, vol. 174, p. 107511, 2024, doi: https://doi.org/10.1016/j.infsof.2024.107511.

Z. Peng, X. Lin, M. Simon, and N. Niu, “Unit and regression tests of scientific software: A study on SWMM,” J Comput Sci, vol. 53, p. 101347, 2021, doi: https://doi.org/10.1016/j.jocs.2021.101347.

A. Mustafa et al., “Automated Test Case Generation from Requirements: A Systematic Literature Review,” Computers, Materials and Continua, vol. 67, no. 2, pp. 1819–1833, 2021, doi: https://doi.org/10.32604/cmc.2021.014391.

Y. Xing, X. Wang, and Q. Shen, “Test case prioritization based on Artificial Fish School Algorithm,” Comput Commun, vol. 180, pp. 295–302, 2021, doi: https://doi.org/10.1016/j.comcom.2021.09.014.

E. Passini, X. Zhou, C. Trovato, O. J. Britton, A. Bueno-Orovio, and B. Rodriguez, “The virtual assay software for human in silico drug trials to augment drug cardiac testing,” J Comput Sci, vol. 52, p. 101202, 2021, doi: https://doi.org/10.1016/j.jocs.2020.101202.

I. Ghani, W. M. N. Wan-Kadir, A. F. Arbain, and N. Ibrahim, “Improved Test Case Selection Algorithm to Reduce Time in Regression Testing,” Computers, Materials and Continua, vol. 72, no. 1, pp. 635–650, 2022, doi: https://doi.org/10.32604/cmc.2022.025027.

V. Garousi, A. B. Keleş, Y. Balaman, Z. Ö. Güler, and A. Arcuri, “Model-based testing in practice: An experience report from the web applications domain,” Journal of Systems and Software, vol. 180, p. 111032, 2021, doi: https://doi.org/10.1016/j.jss.2021.111032.

W. D. van Driel, J. W. Bikker, and M. Tijink, “Prediction of software reliability,” Microelectronics Reliability, vol. 119, p. 114074, 2021, doi: https://doi.org/10.1016/j.microrel.2021.114074.

R. Caballero, E. Martin-Martin, A. Riesco, and S. Tamarit, “A unified framework for declarative debugging and testing,” Inf Softw Technol, vol. 129, p. 106427, 2021, doi: https://doi.org/10.1016/j.infsof.2020.106427.

H. Pei, B. Yin, M. Xie, and K.-Y. Cai, “Dynamic random testing with test case clustering and distance-based parameter adjustment,” Inf Softw Technol, vol. 131, p. 106470, 2021, doi: https://doi.org/10.1016/j.infsof.2020.106470.

T. M. Kanner, A. M. Kanner, and A. V Epishkina, “Algorithm for Optimal and Complete Testing of Software and Hardware Data Security Tools,” Procedia Comput Sci, vol. 190, pp. 408–413, 2021, doi: https://doi.org/10.1016/j.procs.2021.06.049.

DOI: https://doi.org/10.46576/djtechno.v6i1.6040