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Littérature scientifique sur le sujet « Adaptive Software Testing »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Adaptive Software Testing"

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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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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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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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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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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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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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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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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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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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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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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Thèses sur le sujet "Adaptive Software Testing"

Kuo, Fei-Ching. "On adaptive random testing." Australasian Digital Thesis Program, 2006. http://adt.lib.swin.edu.au/public/adt-VSWT20061109.091517.

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Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, 2006.<br>A thesis submitted for the degree of PhD, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2006. Typescript. Bibliography: p. 126-133.

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Arafeen, Md Junaid. "Adaptive Regression Testing Strategy: An Empirical Study." Thesis, North Dakota State University, 2012. https://hdl.handle.net/10365/26525.

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When software systems evolve, different amounts of code modifications can be involved in different versions. These factors can affect the costs and benefits of regression testing techniques, and thus, there may be no single regression testing technique that is the most cost-effective technique to use on every version. To date, many regression testing techniques have been proposed, but no research has been done on the problem of helping practitioners systematically choose appropriate techniques on new versions as systems evolve. To address this problem, we propose adaptive regression testing (A

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Merkel, Robert Graham, and robert merkel@benambra org. "Analysis and enhancements of adaptive random testing." Swinburne University of Technology, 2005. http://adt.lib.swin.edu.au./public/adt-VSWT20050804.144747.

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Random testing is a standard software testing method. It is a popular method for reli-ability assessment, but its use for debug testing has been opposed by some authorities. Random testing does not use any information to guide test case selection, and so, it is argued, testing is less likely to be effective than other methods. Based on the observation that failures often cluster in contiguous regions, Adaptive Random Testing (ART) is a more effective random testing method. While retaining random selection of test cases, selection is guided by the idea that tests should be widely spread throug

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Chodoriwsky, Jacob N. "Error Locating Arrays, Adaptive Software Testing, and Combinatorial Group Testing." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23083.

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Combinatorial Group Testing (CGT) is a process of identifying faulty interactions (“errors”) within a particular set of items. Error Locating Arrays (ELAs) are combinatorial designs that can be built from Covering Arrays (CAs) to not only cover all errors in a system (each involving up to a certain number of items), but to locate and identify the errors as well. In this thesis, we survey known results for CGT, as well as CAs, ELAs, and some other types of related arrays. More importantly, we give several new results. First, we give a new algorithm that can be used to test a system in which eac

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Kuo, Fei-Ching, and n/a. "On adaptive random testing." Swinburne University of Technology, 2006. http://adt.lib.swin.edu.au./public/adt-VSWT20061109.091517.

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Adaptive random testing (ART) has been proposed as an enhancement to random testing for situations where failure-causing inputs are clustered together. The basic idea of ART is to evenly spread test cases throughout the input domain. It has been shown by simulations and empirical analysis that ART frequently outperforms random testing. However, there are some outstanding issues on the cost-effectiveness and practicality of ART, which are the main foci of this thesis. Firstly, this thesis examines the basic factors that have an impact on the faultdetection effectiveness of adaptive random testi

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Towey, David Peter. "Studies of different variations of Adaptive Random Testing." Thesis, View the Table of Contents & Abstract, 2006. http://sunzi.lib.hku.hk/hkuto/record/B3551212X.

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Liu, Ning Lareina. "A study on improving adaptive random testing." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36428061.

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Liu, Ning Lareina, and 劉寧. "A study on improving adaptive random testing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36428061.

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Liu, Huai. "On even spread of test cases in adaptive random testing." Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/40129.

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Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, 2008.<br>A thesis submitted for the degree of Doctor of Philosophy, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2008. Typescript. Bibliography: p. 107-123.

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Abdullah, Sophiana Chua. "Student modelling by adaptive testing : a knowledge-based approach." Thesis, University of Kent, 2003. https://kar.kent.ac.uk/13956/.

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An adaptive test is one in which the number of test items and the order in which the items are presented are computed during the delivery of the test so as to obtain an accurate estimate of a student's knowledge, with a minimum number of test items. This thesis is concerned with the design and development of computerised adaptive tests for use within educational settings. Just as, in the same setting, intelligent tutoring systems are designed to emulate human tutors, adaptive testing systems can be designed to mimic effective informal examiners. The thesis focuses on the role of adaptive testi

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Livres sur le sujet "Adaptive Software Testing"

Petsios, Theofilos. Compiler-assisted Adaptive Software Testing. [publisher not identified], 2018.

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Corporation, Springhouse, ed. Computerized adaptive testing (CAT) disk for NCLEX-PN. Springhouse Corp., 1994.

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Computerized adaptive testing (CAT) disk for NCLEX-RN. Springhouse Corp., 1994.

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Stewart, James Michael, Libby Chovanec, and Ramesh Chandak. MCSE IIS 4 Exam Cram Adaptive Testing Edition: Exam: 70-087. 2nd ed. Coriolis Group Books, 1999.

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Hudson, Kurt, James Michael Stewart, and Gary Novosel. MCSE TCP/IP Exam Cram Adaptive Testing Edition: Exam: 70-059. 2nd ed. Coriolis Group Books, 1999.

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Chapitres de livres sur le sujet "Adaptive Software Testing"

Groz, Roland, Adenilso Simao, and Catherine Oriat. "Adaptive Localizer Based on Splitting Trees." In Testing Software and Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67549-7_21.

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Güniçen, Canan, Guy-Vincent Jourdan, and Hüsnü Yenigün. "Using Multiple Adaptive Distinguishing Sequences for Checking Sequence Generation." In Testing Software and Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25945-1_2.

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Petrenko, Alexandre, and Nina Yevtushenko. "Adaptive Testing of Deterministic Implementations Specified by Nondeterministic FSMs." In Testing Software and Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24580-0_12.

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Sachtleben, Robert, Robert M. Hierons, Wen-ling Huang, and Jan Peleska. "A Mechanised Proof of an Adaptive State Counting Algorithm." In Testing Software and Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31280-0_11.

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Sachtleben, Robert. "An Executable Mechanised Formalisation of an Adaptive State Counting Algorithm." In Testing Software and Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64881-7_15.

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Kushik, Natalia, Khaled El-Fakih, and Nina Yevtushenko. "Adaptive Homing and Distinguishing Experiments for Nondeterministic Finite State Machines." In Testing Software and Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41707-8_3.

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Mayer, Johannes. "Adaptive Random Testing by Bisection and Localization." In Formal Approaches to Software Testing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759744_6.

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Chen, T. Y., De Hao Huang, and Zhi Quan Zhou. "Adaptive Random Testing Through Iterative Partitioning." In Reliable Software Technologies – Ada-Europe 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11767077_13.

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Kushik, Natalia, and Hüsnü Yenigün. "Heuristics for Deriving Adaptive Homing and Distinguishing Sequences for Nondeterministic Finite State Machines." In Testing Software and Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25945-1_15.

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Mayer, Johannes. "Adaptive Random Testing by Bisection with Restriction." In Formal Methods and Software Engineering. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11576280_18.

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Actes de conférences sur le sujet "Adaptive Software Testing"

Martou, Pierre, Benoît Duhoux, Kim Mens, and Axel Legay. "Testing Tool for Combinatorial Transition Testing in Dynamically Adaptive Software Systems." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962493.

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Ying, Zhihao, Dave Towey, Tsong Yueh Chen, and Zhi Quan Zhou. "MT-PART: Metamorphic-Testing-Based Adaptive Random Testing Through Partitioning." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00158.

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Huang, Yingxiang, Minyan Lu, and Xun Long. "Semantic-Guided Adaptive Exploration Testing for GUI Software." In 2024 11th International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2024. https://doi.org/10.1109/dsa63982.2024.00028.

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Li, Ruifeng, Yanwen Liu, Chunjia Zhu, Zenghui Zhou, and Zheng Zheng. "Adaptive Metamorphic Testing for Object Detection Systems." In 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). IEEE, 2024. http://dx.doi.org/10.1109/qrs-c63300.2024.00071.

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Silva, Samira, Ricardo Caldas, Patrizio Pelliccione, and Antonia Bertolino. "An Adaptive Testing Approach Based on Field Data." In 2025 IEEE/ACM International Conference on Automation of Software Test (AST). IEEE, 2025. https://doi.org/10.1109/ast66626.2025.00013.

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Asgari, Ali, Antonio Guerriero, Roberto Pietrantuono, and Stefano Russo. "Adaptive Probabilistic Operational Testing for Large Language Models Evaluation." In 2025 IEEE/ACM International Conference on Automation of Software Test (AST). IEEE, 2025. https://doi.org/10.1109/ast66626.2025.00017.

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Yoon, Juyeon, Robert Feldt, and Shin Yoo. "Adaptive Testing for LLM-Based Applications: A Diversity-Based Approach." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962467.

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Mani, Nariman, and Salma Attaranasl. "Adaptive Test Healing using LLM/GPT and Reinforcement Learning." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962516.

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McIntyre-Garcia, Cristopher, Adrien Heymans, Beril Borali, Won-Sook Lee, and Shiva Nejati. "Generating Minimalist Adversarial Perturbations to Test Object-Detection Models: An Adaptive Multi-Metric Evolutionary Search Approach." In 2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2024. http://dx.doi.org/10.1109/icstw60967.2024.00015.

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Cai, Kai-Yuan, T. Y. Chen, Yong-Chao Li, Wei-Yi Ning, and Y. T. Yu. "Adaptive testing of software components." In the 2005 ACM symposium. ACM Press, 2005. http://dx.doi.org/10.1145/1066677.1067011.

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Rapports d'organisations sur le sujet "Adaptive Software Testing"

Kim, Sour, Phalla Chem, Sovannarith So, Sean Somatra Kim, and Sokhem Pech. Methods and Tools Applied for Climate Change Vulnerability and Adaptation Assessment in Cambodia’s Tonle Sap Basin. Cambodia Development Resource Institute, 2014. https://doi.org/10.64202/wp.97.201407.

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Cambodia is highly susceptible to natural disasters due to the frequency and intensity of extreme climatic events, primarily floods, droughts and windstorms, but also in particular to increased climate variability.These disasters and climate-related hazardshave exacted huge socioeconomic costs on the country’s economy and people’s livelihoods, especially in the last decade. It isimportant, therefore, to understand not only the level of impactbut also the vulnerability and the capacity of people to adapt to these hazards. This emphasises the needfor vulnerability and adaptation assessmentsthat

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