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Статті в журналах з теми "Adaptive Software Testing"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Halimovich, Yuldashev Abdusamat, and Ermatov Axror Baxtiyorjon Ogli. "Increasing Learning Efficiency Using Adaptive Testing Technology." American Journal of Engineering And Techonology 03, no. 02 (2021): 31–41. http://dx.doi.org/10.37547/tajet/volume03issue02-05.

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Анотація:

Purpose: The article describes a set of software developed for adaptive testing technology in the implementation of an objective assessment of students' knowledge. There is also information about the possibility of computerizing education, reducing the unproductive live work of teachers, preserving the methodological potential of experienced professors, installing computer software for management. Methods: It is noted that the experiments were carried out by 2nd year students of the Andijan Machine-Building Institute in the direction of "Ground transport systems and their operation." Results:

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2

Cai, Kai-Yuan. "Optimal software testing and adaptive software testing in the context of software cybernetics." Information and Software Technology 44, no. 14 (2002): 841–55. http://dx.doi.org/10.1016/s0950-5849(02)00108-8.

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3

Junpeng Lv, Hai Hu, Kai-Yuan Cai, and Tsong Yueh Chen. "Adaptive and Random Partition Software Testing." IEEE Transactions on Systems, Man, and Cybernetics: Systems 44, no. 12 (2014): 1649–64. http://dx.doi.org/10.1109/tsmc.2014.2318019.

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4

HU, HAI, CHANG-HAI JIANG, and KAI-YUAN CAI. "AN IMPROVED APPROACH TO ADAPTIVE TESTING." International Journal of Software Engineering and Knowledge Engineering 19, no. 05 (2009): 679–705. http://dx.doi.org/10.1142/s0218194009004349.

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Adaptive testing is the counterpart of adaptive control in software testing. It means that software testing strategy should be adjusted on-line by using the testing data collected during software testing as our understanding of the software under test is improved. Previous studies on adaptive testing involved a simplified Controlled Markov Chain (CMC) model for software testing which employs several unrealistic assumptions. In this paper we propose a new adaptive software testing approach in the context of an improved and namely, general CMC model which aims to eliminate such threats to validi

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5

Cai, Kai-Yuan, Bo Gu, Hai Hu, and Yong-Chao Li. "Adaptive software testing with fixed-memory feedback." Journal of Systems and Software 80, no. 8 (2007): 1328–48. http://dx.doi.org/10.1016/j.jss.2006.11.008.

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6

CAI, KAI-YUAN, TSONG YUEH CHEN, YONG-CHAO LI, YUEN TAK YU, and LEI ZHAO. "ON THE ONLINE PARAMETER ESTIMATION PROBLEM IN ADAPTIVE SOFTWARE TESTING." International Journal of Software Engineering and Knowledge Engineering 18, no. 03 (2008): 357–81. http://dx.doi.org/10.1142/s0218194008003696.

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Software cybernetics is an emerging area that explores the interplay between software and control. The controlled Markov chain (CMC) approach to software testing supports the idea of software cybernetics by treating software testing as a control problem, where the software under test serves as a controlled object modeled by a controlled Markov chain and the software testing strategy serves as the corresponding controller. The software under test and the corresponding software testing strategy form a closed-loop feedback control system. The theory of controlled Markov chains is used to design a

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7

Wu, Huayao, Changhai Nie, Justyna Petke, Yue Jia, and Mark Harman. "An Empirical Comparison of Combinatorial Testing, Random Testing and Adaptive Random Testing." IEEE Transactions on Software Engineering 46, no. 3 (2020): 302–20. http://dx.doi.org/10.1109/tse.2018.2852744.

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8

Cai, Kai-Yuan, Yong-Chao Li, and Ke Liu. "Optimal and adaptive testing for software reliability assessment." Information and Software Technology 46, no. 15 (2004): 989–1000. http://dx.doi.org/10.1016/j.infsof.2004.07.006.

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9

Hu, Hai, Chang-Hai Jiang, Kai-Yuan Cai, W. Eric Wong, and Aditya P. Mathur. "Enhancing software reliability estimates using modified adaptive testing." Information and Software Technology 55, no. 2 (2013): 288–300. http://dx.doi.org/10.1016/j.infsof.2012.08.012.

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10

Cheng, Yiling. "Concerto Software for Computerized Adaptive Testing – Free Version." Measurement: Interdisciplinary Research and Perspectives 21, no. 3 (2023): 194–202. http://dx.doi.org/10.1080/15366367.2023.2187274.

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11

Ihor, HUNKO. "Adaptive Approaches to Software Testing with Embedded Artificial Intelligence in Dynamic Environments." International Journal of Current Science Research and Review 08, no. 05 (2025): 2036–51. https://doi.org/10.5281/zenodo.15344813.

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Abstract : Artificial intelligence (AI) is rapidly being integrated into application domains such as autonomous vehicles, health care, and cybersecurity; therefore, the requirements for dependable and robust AI-embedded systems are more pressing in these dynamic environments characterized by unpredictable variations in operational conditions. The traditional software testing methodologies that depend on static test cases and a predetermined set of scenarios usually fail to tackle the complexity of modern AI applications, resulting in undetected defects and security vulnerabilities. This study

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12

Agarwal, Rahul, Stephen H. Edwards, and Manuel A. Pérez-Quiñones. "Designing an adaptive learning module to teach software testing." ACM SIGCSE Bulletin 38, no. 1 (2006): 259–63. http://dx.doi.org/10.1145/1124706.1121420.

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13

Sun, Chang-ai, Hepeng Dai, Huai Liu, Tsong Yueh Chen, and Kai-Yuan Cai. "Adaptive Partition Testing." IEEE Transactions on Computers 68, no. 2 (2019): 157–69. http://dx.doi.org/10.1109/tc.2018.2866040.

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14

鄭淑臻, 鄭淑臻, Yu-Ping Cheng Shu-Chen Cheng, and 黃悅民 Yu-Ping Cheng. "To Implement Computerized Adaptive Testing by Automatically Adjusting Item Difficulty Index on Adaptive English Learning Platform." 網際網路技術學刊 22, no. 7 (2021): 1599–607. http://dx.doi.org/10.53106/160792642021122207013.

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15

Jawalkar, Santosh Kumar. "Machine Learning in QA: A Vision for Predictive and Adaptive Software Testing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 07 (2021): 1–7. https://doi.org/10.55041/ijsrem9725.

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Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning

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16

Cen, Ye. "Artificial Intelligence Applications in Software Testing." Applied and Computational Engineering 118, no. 1 (2025): 186–91. https://doi.org/10.54254/2755-2721/2025.20939.

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Software testing plays a critical role in ensuring the reliability, functionality, and performance of software systems. However, traditional testing methods often fall short in addressing the demands of modern complex applications due to their limited scalability and adaptability. Artificial intelligence (AI) has emerged as a transformative force in software testing, providing tools for automation, defect prediction, and test optimization. This article explores the application of AI in software testing, emphasizing its role in enhancing test coverage, reducing human intervention, and enabling

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17

Veres, S. M., and J. P. Norton. "Software Package for Testing and Tuning of Robust Adaptive Controllers." IFAC Proceedings Volumes 24, no. 4 (1991): 325–29. http://dx.doi.org/10.1016/s1474-6670(17)54292-5.

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18

Lv, Junpeng, Bei-Bei Yin, and Kai-Yuan Cai. "Estimating confidence interval of software reliability with adaptive testing strategy." Journal of Systems and Software 97 (November 2014): 192–206. http://dx.doi.org/10.1016/j.jss.2014.08.004.

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19

Cai, Kai-Yuan, Chang-Hai Jiang, Hai Hu, and Cheng-Gang Bai. "An experimental study of adaptive testing for software reliability assessment." Journal of Systems and Software 81, no. 8 (2008): 1406–29. http://dx.doi.org/10.1016/j.jss.2007.11.721.

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20

Li, Yaohang, and Yong-Duan Song. "An adaptive and trustworthy software testing framework on the grid." Journal of Supercomputing 46, no. 2 (2007): 124–38. http://dx.doi.org/10.1007/s11227-007-0160-2.

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21

Forti, Stefano. "An Interview with Tsong Yueh Chen - 2024 SIGSOFT Awardee." ACM SIGSOFT Software Engineering Notes 49, no. 3 (2024): 21–22. http://dx.doi.org/10.1145/3672089.3672095.

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Tsong Yueh Chen received the 2024 SIGSOFT Outstanding Research Award for contributions to software testing through the invention and development of metamorphic testing. He is currently a Professor of Software Engineering at the Department of Computer Science and Software Engineering, Swinburne University of Technology, Australia. Previously, he pursued a PhD degree from the University of Melbourne and an MSc degree and DIC from the Imperial College of Science and Technology, London, U.K. Before joining Swinburne University of Technology, he has lectured at The University of Hong Kong and The U

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22

CHAN, KWOK PING, TSONG YUEH CHEN, and DAVE TOWEY. "RESTRICTED RANDOM TESTING: ADAPTIVE RANDOM TESTING BY EXCLUSION." International Journal of Software Engineering and Knowledge Engineering 16, no. 04 (2006): 553–84. http://dx.doi.org/10.1142/s0218194006002926.

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Restricted Random Testing (RRT) is a new method of testing software that improves upon traditional Random Testing (RT) techniques. Research has indicated that failure patterns (portions of an input domain which, when executed, cause the program to fail or reveal an error) can influence the effectiveness of testing strategies. For certain types of failure patterns, it has been found that a widespread and even distribution of test cases in the input domain can be significantly more effective at detecting failure compared with ordinary RT. Testing methods based on RT, but which aim to achieve eve

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23

Yang, Shun Kun, and Fu Ping Zeng. "Improved Genetic Algorithms for Software Testing Cases Generation." Applied Mechanics and Materials 380-384 (August 2013): 1464–68. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1464.

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In order to realize the adaptive Genetic Algorithms to balance the contradiction between algorithm convergence rate and algorithm accuracy for automatic generation of software testing cases, improved Genetic Algorithms is proposed for different aspects. Orthogonal method and Equivalence partitioning are employed together to make the initial testing population more effective with more reasonable coverage; Genetic operators of Crossover and Mutation is defined adaptively by the dynamic adjustment according to multi-objective Fitness function, which can guide the testing process more properly and

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24

Syang, Angel, and Nell B. Dale. "Computerized adaptive testing in computer science." ACM SIGCSE Bulletin 25, no. 1 (1993): 53–56. http://dx.doi.org/10.1145/169073.169109.

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25

Spieker, Helge, and Arnaud Gotlieb. "Adaptive metamorphic testing with contextual bandits." Journal of Systems and Software 165 (July 2020): 110574. http://dx.doi.org/10.1016/j.jss.2020.110574.

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26

Sistla, Swetha. "The Future of Automation Testing: From SDET to Autonomous Testing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–7. http://dx.doi.org/10.55041/ijsrem6802.

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Automation testing has rapidly evolved from traditional manual methods to sophisticated AI-driven autonomous testing frameworks. In this paper, we have discussed the evolution of the automation testing role and, in particular, the transition from the SDET, Software Development Engineer in Test, to a fully autonomous testing solution. We look at a set of emerging technologies-artificial intelligence, machine learning, and advanced analytics-enabling the creation of self-healing test scripts, predictive analysis, and real-time feedback loops. Autonomous testing promises not just efficiency but a

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27

Nie, Changhai, Huayao Wu, Xintao Niu, Fei-Ching Kuo, Hareton Leung, and Charles J. Colbourn. "Combinatorial testing, random testing, and adaptive random testing for detecting interaction triggered failures." Information and Software Technology 62 (June 2015): 198–213. http://dx.doi.org/10.1016/j.infsof.2015.02.008.

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28

Zhang, Zhaoxia. "Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm." International Journal of Intelligent Information Technologies 20, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijiit.344024.

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Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adapt

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29

Manuja, Bandal. "AI-Enabled Adaptive Fault Injection for Self-Regulating Software Testing in AWS Cloud Platforms." Journal of Scientific and Engineering Research 9, no. 12 (2022): 236–45. https://doi.org/10.5281/zenodo.15044761.

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Testing software in cloud environments, especially within AWS infrastructures, presents distinct obstacles due to dynamic resource allocation, decentralized architectures, and unpredictable execution conditions. Conventional testing methods often fail to detect cloud-specific faults such as transient errors, race conditions in autoscaling, and inconsistencies in distributed systems. This paper introduces AI-Enabled Adaptive Fault Injection (AIAFI), a novel autonomous testing framework specifically designed for AWS-based applications. AIAFI autonomously detects, injects, and modifies fault scen

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30

Bai, Wen Le, Yong Mei Zhang, and Bin Song. "Study on Optimization of Software Regressive Testing Based on RBF Neural Networks." Applied Mechanics and Materials 268-270 (December 2012): 1714–17. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1714.

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In order to reduce times of software regression testing, a new research idea and method is proposed based on RBFN (Radial Basis Function Network). Using the adaptive ability of network study, regression testing is optimized by its learning strategy. The simulation results demonstrate the new method can forecast regressive testing effectively, and implement very little error. It means an important meaning for developing new effective method of soft testing in the future.

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31

Chalhoub–Deville, Micheline, and Craig Deville. "COMPUTER ADAPTIVE TESTING IN SECOND LANGUAGE CONTEXTS." Annual Review of Applied Linguistics 19 (January 1999): 273–99. http://dx.doi.org/10.1017/s0267190599190147.

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The widespread accessibility to large, networked computer labs at educational sites and commercial testing centers, coupled with fast-paced advances in both computer technology and measurement theory, along with the availability of off-the-shelf software for test delivery, all help to make the computerized assessment of individuals more efficient and accurate than assessment using traditional paper-and-pencil (P&P) tests. Computer adaptive testing (CAT) is a form of computerized assessment that has achieved a strong foothold in licensure and certification testing and is finding greater app

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32

Chen, Tsong Yueh, Fei-Ching Kuo, and Huai Liu. "Adaptive random testing based on distribution metrics." Journal of Systems and Software 82, no. 9 (2009): 1419–33. http://dx.doi.org/10.1016/j.jss.2009.05.017.

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33

Chen, Jinfu, Patrick Kwaku Kudjo, Zufa Zhang, et al. "A Modified Similarity Metric for Unit Testing of Object-Oriented Software Based on Adaptive Random Testing." International Journal of Software Engineering and Knowledge Engineering 29, no. 04 (2019): 577–606. http://dx.doi.org/10.1142/s0218194019500244.

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Finding an effective method for testing object-oriented software (OOS) has proven elusive in the software community due to the rapid development of object-oriented programming (OOP) technology. Although significant progress has been made by previous studies, challenges still exist in relation to the object distance measurement of OOS using Adaptive Random Testing (ART). This is partly due to the unique features of OOS such as encapsulation, inheritance and polymorphism. In a previous work, we proposed a new similarity metric called the Object and Method Invocation Sequence Similarity (OMISS) m

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34

Burr, Steven A., Thomas Gale, Jolanta Kisielewska, et al. "A narrative review of adaptive testing and its application to medical education." MedEdPublish 13 (October 24, 2023): 221. http://dx.doi.org/10.12688/mep.19844.1.

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Adaptive testing has a long but largely unrecognized history. The advent of computer-based testing has created new opportunities to incorporate adaptive testing into conventional programmes of study. Relatively recently software has been developed that can automate the delivery of summative assessments that adapt by difficulty or content. Both types of adaptive testing require a large item bank that has been suitably quality assured. Adaptive testing by difficulty enables more reliable evaluation of individual candidate performance, although at the expense of transparency in decision making, a

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35

Huang, Rubing, Weifeng Sun, Haibo Chen, Chenhui Cui, and Ning Yang. "A nearest-neighbor divide-and-conquer approach for adaptive random testing." Science of Computer Programming 215 (March 2022): 102743. http://dx.doi.org/10.1016/j.scico.2021.102743.

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36

Srinivasa Rao Kongarana. "Machine Learning-Based Adaptive Test Sequence Recommendation System for Regression Testing ATSRS." Journal of Information Systems Engineering and Management 10, no. 20s (2025): 814–30. https://doi.org/10.52783/jisem.v10i20s.3261.

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In software testing, especially regression testing, selecting test sequences manually can take a lot of time and may lead to mistakes, making the process less effective. This study introduces the Adaptive Test Sequence Recommendation System (ATSRS), which uses machine learning to suggest the best test sequences. The system analyzes how different parts of the software interact and uses this information to make its recommendations. It continuously learns and improves by fine-tuning its data using linear. The ATSRS is trained with this improved data using a boosting method that combines several s

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37

Kalache, Ayyoub, Mourad Badri, Farid Mokhati, and Mohamed Chaouki Babahenini. "A testing framework for JADE agent-based software." Multiagent and Grid Systems 19, no. 1 (2023): 61–98. http://dx.doi.org/10.3233/mgs-230023.

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Multi-agent systems are proposed as a solution to mitigate nowadays software requirements: open and distributed architectures with dynamic and adaptive behaviour. Like any other software, multi-agent systems development process is error-prone; thus testing is a key activity to ensure the quality of the developed product. This paper sheds light on agent testing as it is the primary artefact for any multi-agent system’s testing process. A framework called JADE Testing Framework (JTF) for JADE platform’s agent testing is proposed. JTF allows testing agents at two levels: unit (inner-components) a

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38

Dalwani, Ms Ranjana, and Prof Makrand Samvatsar. "Design & Development of Model Based Adaptive Testing for Software Quality Assurance." IJARCCE 6, no. 4 (2017): 99–104. http://dx.doi.org/10.17148/ijarcce.2017.6420.

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39

Lv, Junpeng, Bei-Bei Yin, and Kai-Yuan Cai. "On the Asymptotic Behavior of Adaptive Testing Strategy for Software Reliability Assessment." IEEE Transactions on Software Engineering 40, no. 4 (2014): 396–412. http://dx.doi.org/10.1109/tse.2014.2310194.

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40

Lilley, M., T. Barker, and C. Britton. "The development and evaluation of a software prototype for computer-adaptive testing." Computers & Education 43, no. 1-2 (2004): 109–23. http://dx.doi.org/10.1016/j.compedu.2003.12.008.

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41

Huang, Rubing, Jinfu Chen, and Yansheng Lu. "Adaptive Random Testing with Combinatorial Input Domain." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/843248.

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Анотація:

Random testing (RT) is a fundamental testing technique to assess software reliability, by simply selecting test cases in a random manner from the whole input domain. As an enhancement of RT, adaptive random testing (ART) has better failure‐detection capability and has been widely applied in different scenarios, such as numerical programs, some object‐oriented programs, and mobile applications. However, not much work has been done on the effectiveness of ART for the programs with combinatorial input domain (i.e., the set of categorical data). To extend the ideas to the testing for combinatorial

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42

Jainik Sudhanshubhai Patel. "AI-Driven Test Automation: Transforming Software Quality Engineering." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 339–47. https://doi.org/10.32996/jcsts.2025.7.2.35.

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Анотація:

The integration of artificial intelligence into test automation represents a paradigm shift in software quality engineering, addressing longstanding challenges of traditional testing methods. As applications grow increasingly complex with microservices architectures, cloud-native components, and frequent deployment cycles, AI-driven testing emerges as a solution to the brittleness and maintenance overhead of conventional approaches. By leveraging machine learning, natural language processing, computer vision, and self-learning systems, organizations can reduce script maintenance efforts while

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43

Sanatang, Sanatang, and Muhammad Fajar B. "Sistem Tes Interaktif Berbasis Computerized Adaptive Testing (CAT)." Jurnal MediaTIK 4, no. 1 (2021): 30. http://dx.doi.org/10.26858/jmtik.v4i1.19726.

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Анотація:

Penelitian bertujuan untuk membuat sistem tes interaktif berbasis computerized adaptive testing (CAT). Penelitian ini merupakan jenis penelitian pengembangan perangkat lunak (software development) dengan menggunakan metode SDLC (Systems Development Life Cycle) dan menggunakan model Waterfall. Hasil dari penelitian ini adalah sebuah sistem tes interaktif berbasis computerized adaptive testing (CAT) yang dapat digunakan pada lembaga Testing Centre untuk mengefektifkan dan mengefisienkan kinerja lembaga Testing Centre dalam pelaksanaan tes adaptif berbasis komputer. Tanggapan responden admin terh

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44

Veeranna, Kotagi. "Adaptive and Predictive Testing Frameworks Using Chaos Engineering and Deep Learning for Enterprise QA." Recent Trends in Data Knowledge Discovery and Data Mining 1, no. 1 (2025): 16–22. https://doi.org/10.5281/zenodo.15187073.

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<em>As enterprise software systems grow in complexity and scale, traditional quality assurance (QA) methods fall short in predicting failures and ensuring system resilience. This research proposes an innovative QA paradigm that integrates chaos engineering with deep learning to create adaptive and predictive testing frameworks. Chaos engineering introduces controlled disruptions to identify system weaknesses under real-world stress, while deep learning models analyze test outcomes to anticipate failure points and adapt testing strategies dynamically. The proposed framework supports continuous

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Demir, Hümeyra, and Selahattin Gelbal. "A Systematic Review on Computerized Adaptive Testing." Erzincan Üniversitesi Eğitim Fakültesi Dergisi 27, no. 1 (2025): 137–50. https://doi.org/10.17556/erziefd.1577880.

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The aim of this research is to systematically review studies related to Computerized Adaptive Testing (CAT). Following systematic review guidelines, 110 articles were evaluated to seek answers to the established questions. These articles were analyzed based on their objectives, results, and recommendations, leading to a general conclusion. The compiled articles highlighted that innovative methods for CAT emerged as the most researched area. Within these innovative methods, the most studied topics were item selection algorithms and cognitive diagnosis computerized adaptive testing (CD-CAT). The

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Peng Wang, Haiqi Dai, and Shuliang Ding. "Computerized Adaptive Testing in Chinese Mainland: A Review." International Journal of Digital Content Technology and its Applications 4, no. 6 (2010): 94–99. http://dx.doi.org/10.4156/jdcta.vol4.issue6.11.

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Noijons, José. "Testing Computer Assisted Language Testing." CALICO Journal 12, no. 1 (2013): 37–58. http://dx.doi.org/10.1558/cj.v12i1.37-58.

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Much computer assisted language learning (CALL) material that includes tests and exercises looks attractive enough but is clearly lacking in terms of validation: the possibilities of the computer and the inventiveness of the programmers mainly determine the format of tests and exercises, causing possible harm to a fair assessment of pupils' language abilities. This article begins with a definition of computer assisted language testing (CALT), followed by a discussion of the various processes involved. E3oth advantages and disadvantages of CALT are outlined. Psychometric aspects of comput

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Selay, Elmin, Zhi Quan Zhou, Tsong Yueh Chen, and Fei-Ching Kuo. "Adaptive Random Testing in Detecting Layout Faults of Web Applications." International Journal of Software Engineering and Knowledge Engineering 28, no. 10 (2018): 1399–428. http://dx.doi.org/10.1142/s0218194018500407.

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As part of a software testing process, output verification poses a challenge when the output is not numeric or textual, such as graphical. The industry practice of using human oracles (testers) to observe and verify the correctness of the actual results is both expensive and error-prone. In particular, this practice is usually unsustainable when developing web applications — the most popular software of our era. This is because web applications change frequently due to the fast-evolving requirements amid popular demand. To improve the cost effectiveness of browser output verification, in this

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Huang, Rubing, Haibo Chen, Weifeng Sun, and Dave Towey. "Candidate test set reduction for adaptive random testing: An overheads reduction technique." Science of Computer Programming 214 (February 2022): 102730. http://dx.doi.org/10.1016/j.scico.2021.102730.

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Prabowo, Mei, and Mohd Syafiq. "PENGEMBANGAN MEDIA PEMBELAJARAN BILANGAN CACAH BERBASIS ANDORID UNTUK SISWA SD/MI DENGAN METODE ADAPTIVE SOFTWARE DEVELOPMENT." JURNAL AKADEMIKA 16, no. 2 (2024): 70–75. http://dx.doi.org/10.53564/akademika.v16i2.1234.

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Development of Andorid-Based Learning Media on Whole Numbers for Elementary/MI Students Using the Adaptive Software Development Method, The purpose of this study is to develop an Android-based whole number learning media application for SD/MI using the Adaptive Software Development method. The development of this application is divided into 2 iterations. Functional testing method using black-box testing technique. From the results of black-box testing, it show that all features/functions run well and smoothly. The usability method is used to see user ratings of this learning media application.

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