Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

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. Nevertheles...

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Autores principales:	Tamas Galli, Francisco Chiclana, Francois Siewe
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	software product quality model quality assessment execution tracing logging execution tracing quality logging quality Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/91032ff7191b491dad99f9309b5babda
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spelling	oai:doaj.org-article:91032ff7191b491dad99f9309b5babda2021-11-11T18:21:05ZGenetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality10.3390/math92128222227-7390https://doaj.org/article/91032ff7191b491dad99f9309b5babda2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2822https://doaj.org/toc/2227-7390Execution 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.Tamas GalliFrancisco ChiclanaFrancois SieweMDPI AGarticlesoftware product quality modelquality assessmentexecution tracingloggingexecution tracing qualitylogging qualityMathematicsQA1-939ENMathematics, Vol 9, Iss 2822, p 2822 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	software product quality model quality assessment execution tracing logging execution tracing quality logging quality Mathematics QA1-939
spellingShingle	software product quality model quality assessment execution tracing logging execution tracing quality logging quality Mathematics QA1-939 Tamas Galli Francisco Chiclana Francois Siewe Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
description	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.
format	article
author	Tamas Galli Francisco Chiclana Francois Siewe
author_facet	Tamas Galli Francisco Chiclana Francois Siewe
author_sort	Tamas Galli
title	Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
title_short	Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
title_full	Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
title_fullStr	Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
title_full_unstemmed	Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
title_sort	genetic algorithm-based fuzzy inference system for describing execution tracing quality
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/91032ff7191b491dad99f9309b5babda
work_keys_str_mv	AT tamasgalli geneticalgorithmbasedfuzzyinferencesystemfordescribingexecutiontracingquality AT franciscochiclana geneticalgorithmbasedfuzzyinferencesystemfordescribingexecutiontracingquality AT francoissiewe geneticalgorithmbasedfuzzyinferencesystemfordescribingexecutiontracingquality
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Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

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