hW-inference: A heuristic approach to retrieve models through black box testing

Groz, Roland; Brémond, Nicolas; Simão, Adenilso da Silva; Oriat, Catherine

doi:10.1016/j.jss.2019.110426

Cited by 10 publications

(15 citation statements)

References 36 publications

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“…In this way, we can avoid logical errors due to excessive input parameters, and we can also avoid operational errors due to excessive input parameters. Black-box testing is to detect whether there are bugs in the internal code of the software or to check the integrity and correctness of the data when it is running [ 14 ]. Black-box testing is an effective way to improve system performance, reduce risk, and improve software functionality.…”

Section: Testing Results Of the Legal Information Retrieval Systemmentioning

confidence: 99%

Design of a Computer-Based Legal Information Retrieval System

Shi

2022

Computational Intelligence and Neuroscience

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In today’s society, people’s lives are increasingly inseparable from computer information. Due to the continuous improvement of technology and the rapid development of internet technology, the network environment is becoming more and more complex, which makes it easy to cause loopholes in the information retrieval system when people use the network. Therefore, it is especially important to search for legal knowledge by computer. In order to adapt to this change and demand, we need a retrieval system to provide the corresponding search function, legal information content, and management and other services, so as to achieve the purpose of computer legal information retrieval. The legal information retrieval system is computer based, draws conclusions from the analysis of relevant data, and then applies them to judicial trial cases, criminal investigations, and other fields to provide a reference for relevant legal issues. The system is designed to combine computer technology with a criminal investigation and other fields, and then analyze the data to draw the corresponding conclusions. The retrieval algorithms used are mainly image and content retrieval algorithms, and image retrieval algorithms mainly use image segmentation technology, while content retrieval algorithms mainly use the cuckoo algorithm. At present, the information construction and economic and social development in China have become one of the issues of common concern and need to be solved by all countries in the world. The study of the legal information retrieval system is of great importance in the construction of information technology and the development of economic society.

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Section: Testing Results Of the Legal Information Retrieval Systemmentioning

confidence: 99%

Design of a Computer-Based Legal Information Retrieval System

Shi

2022

Computational Intelligence and Neuroscience

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“…Its modular design provides a solid basis for experimentation with new learning algorithms, equivalence oracles, and counterexample processing. In future, we intend to extend these functionalities, with SAT-based learning [15] and learning without reset [14]. We hope that the community will recognize AALpy as an attractive foundation for further research, and welcome suggestions and extensions.…”

Section: Discussionmentioning

confidence: 99%

AALpy: an active automata learning library

Muškardin

Aichernig

Pill

et al. 2022

Innovations Syst Softw Eng

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AALpy is an extensible open-source Python library providing efficient implementations of active automata learning algorithms for deterministic, non-deterministic, and stochastic systems. We put a special focus on the conformance testing aspect in active automata learning, as well as on an intuitive and seamlessly integrated interface for learning automata characterizing real-world reactive systems. In this article, we present AALpy’s core functionalities, illustrate its usage via examples, and evaluate its learning performance. Finally, we present selected case studies on learning models of various types of systems with AALpy.

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“…Within the context of model inference, the active learning approach depends on a communication channel with the system so that interacting and testing it to learn its behavior is possible, which generates costs for its preparation and execution, besides there are situations in which such communication is not possible (BOLLIG et al, 2009;GROZ et al, 2020). Another possibility is passive model inference, which does not require access to the system since it is executed based on a finite set of examples of the system's behavior, which can be positive or negative examples.…”

Section: Contextualizationmentioning

confidence: 99%