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.
Highlights
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
Execution tracing quality has not been described by a quality model, nor is it included in software product quality frameworks in an appropriate manner, which is an impediment while measuring software product quality and formulating quality targets for software products [1,2]
As human experiences and human qualities need to be considered while modelling execution tracing quality, artificial intelligence (AI) methods were used in the present study
Summary
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. As a term, it is used interchangeably with logging in the scope of this study. The ability to describe and assess a software product from any point of view that involves human evaluation results in uncertainty to some extent [3]. The evaluation of how the software operates and relates to its environment and the extent to which the end user is satisfied with it are aspects that cannot be covered by static code analysis, only by human assessment. Modelling the problem requires the implicit uncertainty to be handled
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