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

Tamas Galli, Francisco Chiclana and Francois Siewe
Additional contact information
Tamas Galli: Faculty of Computing, Engineering and Media, Institute of Artificial Intelligence (IAI), De Montfort University, Leicester LE1 9BH, UK
Francisco Chiclana: Faculty of Computing, Engineering and Media, Institute of Artificial Intelligence (IAI), De Montfort University, Leicester LE1 9BH, UK
Francois Siewe: Software Technology Research Laboratory (STRL), Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK

Mathematics, 2021, vol. 9, issue 21, 1-71

Abstract: 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.

Keywords: software product quality model; quality assessment; execution tracing; logging; execution tracing quality; logging quality; fuzzy logic; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/21/2822/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/21/2822/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:21:p:2822-:d:673536

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().