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Systematic literature review protocol. Learning-outcomes and teaching-learning process: a Bloom's taxonomy perspective
Context: The importance of defining learning outcomes and the planning s...
11/20/2019 ∙ by Samuel Sepúlveda, et al. ∙
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Systematization of Vulnerability Discovery Knowledge: Review Protocol
In this report, we describe the review protocol that will guide the syst...
02/08/2019 ∙ by Nuthan Munaiah, et al. ∙
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Systematic Mapping Protocol Feature Modeling Tools
The customers and users need for new products and services according to ...
07/18/2019 ∙ by Samuel Sepúlveda, et al. ∙
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Analyzing the Impact of Automated User Assistance Systems: A Systematic Review
Context: User assistance is generally defined as the guided assistance t...
03/12/2020 ∙ by Murat Acar, et al. ∙
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The Prevalence of Errors in Machine Learning Experiments
Context: Conducting experiments is central to research machine learning ...
09/10/2019 ∙ by Martin Shepperd, et al. ∙
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Ability and Context Based Adaptive System: A Proposal for Machine Learning Approach
When we interact with small screen devices, sometimes we make errors, du...
04/12/2019 ∙ by Elgin Akpınar, et al. ∙
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Towards Evidence-Based Ontology for Supporting Systematic Literature Review
[Background]: Systematic Literature Review (SLR) has become an important...
09/22/2016 ∙ by Yueming Sun, et al. ∙
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Systematic literature review protocol Identification and classification of feature modeling errors
10/28/2020 ∙ by Samuel Sepúlveda, et al. ∙
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Context: The importance of feature modeling languages for software product lines and the planning stage for a systematic literature review. Objective: A protocol for carrying out a systematic literature review about the evidence for identifying and classifying the errors in feature modeling languages. Method: The definition of a protocol to conduct a systematic literature review according to the guidelines of B. Kitchenham. Results: A validated protocol to conduct a systematic literature review. Conclusions: A proposal for the protocol definition of a systematic literature review about the identification and classification of errors in feature modeling was built. Initial results show that the effects and results for solving these errors should be carried out.
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