Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11960/4041
Title: Artificial intelligence applied to software testing: a literature review
Authors: Lima, Rui
Cruz, António Miguel
Ribeiro, Jorge
Keywords: Software testing
Test pattern
Artificial intelligence
Machine learning
Artificial neural network
Genetic algorithm
Issue Date: 2020
Publisher: IEEE
Citation: Lima, R., Cruz, A. M. R., & Ribeiro, J. (2020). Artificial intelligence applied to software testing: a literature review. In A. Rocha, B. Escobar Peréz, F. Garcia Peñalvo, M. Del Mar Miras, & R. Gonçalves (Eds.), 15th Iberian Conference on Information Systems and Technologies (CISTI 2020), 24-27 june, 2020, Sevilha, (pp. 1-6). IEEE. https://doi.org/10.23919/CISTI49556.2020.9141124
Abstract: In recent decades there has been an increasing concern about climate change. Every person is increasingly concerned about global warming and, as a consumer, with their own individual contribute to that issue, wich may be measured by each one’s carbon footprint. In this sense, it is only natural that each person wants to consume products with a lower carbon footprint, meaning with a lower environmental impact. For this, however, consumers need to be able to know the carbon footprint of the products they are buying. This is only possible by having every company tracking and sharing their own products carbon footprint. The blockchain is a distributed technology that allows for registering and sharing information between those companies and the final consumers. The blockchain is being used in many areas as a distributed database, and has some strong points like trust, transparency, security, immutability, durability, disintermediation and others. In this paper the blockchain technology is being used to track and trace back the carbon footprint of products and organizations. More exactly, this paper proposes a smart contract-based platform for the traceability of the carbon footprint of products and organizations.
URI: http://hdl.handle.net/20.500.11960/4041
ISBN: 978-989-54659-0-3
Appears in Collections:ADiT-Lab - Publicações indexadas à WoS/Scopus
ESTG - Publicações indexadas à WoS/Scopus

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