Coupon Subset Collection Problem with Quotas

Shioda, Shigeo

doi:10.1007/s11009-020-09811-z

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Different software testing techniques reported in the literature in the recent years. In this research article, we refers the reader to the most important articles from our overview as [14][15][16].…”

Section: Search Based Software Engineering (Sbse)mentioning

confidence: 99%

Software Testing Based on Research: A Road Map

Salim Abdulla,

Abdul Qader Thabit,

Ahmed Al-Zaidi

2023

ejeee

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This paper serves as a guide for both researchers and students who are new to the research area of Search-Based Package Testing. The application of metaheuristic explore methods in this context specifically pertains to utilizing these algorithms for generating test data. In the realm of software engineering research, software testing emerges as a robust and fertile ground for exploration. The integration of AI methods into software program testing is an evolving research direction. Often, newcomers to this field face challenges due to limited knowledge about the interaction between software testing and artificial intelligence. This paper aims to provide a roadmap for these new researchers or students in the field.

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Section: Search Based Software Engineering (Sbse)mentioning

confidence: 99%

Software Testing Based on Research: A Road Map

Salim Abdulla,

Abdul Qader Thabit,

Ahmed Al-Zaidi

2023

ejeee

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As good as it gets: a scaling comparison of DNA computing, network biocomputing, and electronic computing approaches to an NP-complete problem

et al. 2021

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All known algorithms to solve Nondeterministic Polynomial (NP) Complete problems, relevant to many real-life applications, require the exploration of a space of potential solutions, which grows exponentially with the size of the problem. Since electronic computers can implement only limited parallelism, their use for solving NP-complete problems is impractical for very large instances, and consequently alternative massively parallel computing approaches were proposed to address this challenge. We present a scaling analysis of two such alternative computing approaches, DNA Computing (DNA-C) and Network Biocomputing with Agents (NB-C), compared with Electronic Computing (E-C). The Subset Sum Problem (SSP), a known NP-complete problem, was used as a computational benchmark, to compare the volume, the computing time, and the energy required for each type of computation, relative to the input size. Our analysis shows that the sequentiality of E-C translates in a very small volume compared to that required by DNA-C and NB-C, at the cost of the E-C computing time being outperformed first by DNA-C (linear run time), followed by NB-C. Finally, NB-C appears to be more energy-efficient than DNA-C for some types of input sets, while being less energy-efficient for others, with E-C being always an order of magnitude less energy efficient than DNA-C. This scaling study suggest that presently none of these computing approaches win, even theoretically, for all three key performance criteria, and that all require breakthroughs to overcome their limitations, with potential solutions including hybrid computing approaches.

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Coupon Subset Collection Problem with Quotas

Cited by 2 publications

References 21 publications

Software Testing Based on Research: A Road Map

Software Testing Based on Research: A Road Map

As good as it gets: a scaling comparison of DNA computing, network biocomputing, and electronic computing approaches to an NP-complete problem

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