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Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
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Sentiment Analysis using TF-IDF Weighting of UK MPs’ Tweets on Brexit
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Proportional hesitant 2-tuple linguistic distance measurements and extended VIKOR method: Case study of evaluation and selection of green airport plans
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A Fuzzy Approach to Sentiment Analysis at the Sentence Level
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Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events
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Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Applications in Integrated Diagnosis.
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Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
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Abstract:
open access article ; Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.
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Keyword:
artificial intelligence; execution tracing; execution tracing quality; fuzzy logic; logging; logging quality; quality assessment; software product quality model
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URL: https://doi.org/10.3390/math9212822
https://dora.dmu.ac.uk/handle/2086/21425
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| 10 |
Consistency improvement with a feedback recommendation in personalized linguistic group decision making
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| 12 |
Attitude Quantifier Based Possibility Distribution Generation Method for Hesitant Fuzzy Linguistic Group Decision Making
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| 13 |
Multi-stage consistency optimization algorithm to decision-making with incomplete probabilistic linguistic preference relation
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| 14 |
Revisiting Fuzzy and Linguistic Decision-Making: Scenarios and Challenges for Wiser Decisions in a Better Way
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| 15 |
ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages
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Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information
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| 17 |
Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts
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| 18 |
Type-1 OWA operators in aggregating multiple sources of uncertain information : properties and real world applications
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| 19 |
Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets
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In: ISSN: 0884-8173 ; EISSN: 1098-111X ; International Journal of Intelligent Systems ; https://www.hal.inserm.fr/inserm-03026626 ; International Journal of Intelligent Systems, Wiley, 2019, 34 (6), pp.1261-1280. ⟨10.1002/int.22095⟩ (2019)
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Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects
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