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

dc.cclicenceCC-BYen
dc.contributor.authorGalli, Tamas
dc.contributor.authorChiclana, Francisco
dc.contributor.authorSiewe, Francois
dc.date.acceptance2021-11-06
dc.date.accessioned2021-11-08T15:19:59Z
dc.date.available2021-11-08T15:19:59Z
dc.date.issued2021-11-06
dc.descriptionopen access articleen
dc.description.abstractExecution 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. The supporting dataset can be found at https://zenodo.org/records/5552684.en
dc.funderNo external funderen
dc.identifier.citationGalli, T.; Chiclana, F.; Siewe, F. (2021) Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality. Mathematics, 9, 2822en
dc.identifier.doihttps://doi.org/10.3390/math9212822
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/21425
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherMDPIen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.subjectsoftware product quality modelen
dc.subjectquality assessmenten
dc.subjectexecution tracingen
dc.subjectloggingen
dc.subjectexecution tracing qualityen
dc.subjectlogging qualityen
dc.subjectfuzzy logicen
dc.subjectartificial intelligenceen
dc.titleGenetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Qualityen
dc.typeArticleen

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