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The relationship between the various existing smell taxonomies and the smell impacting factors has been established. The ideology is to identify the most critical smell influencing factors in the vicinity of various software development environments.

To fulfill the said task, the utilization of the amalgamation of two multicriteria decision-making techniques, namely, Entropy method and CODAS method, is presented.

Through this article, the most critical smell impacting criteria with respect to the smell taxonomies is identified. Furthermore, the behaviour of 4 software development principles was then analysed, and their working state has been successfully assessed.

The ideology to study design-related smells in the software system has been studied by a lot of researchers. Some of them have worked upon their detection and the corresponding refactoration process with the help of several algorithms like machine learning and artificial intelligence. But how and to what extent these design-related smells impact the software development environment has remained out of the limelight till now. Through this article, this research gap has been identified, and an attempt to fill it has been made.

Presents results obtained from research of a model of change in condition of systems with a view to raising effectiveness of a technological trial (testing) and to improving preventive maintenance. On the basis of this model, it follows that maximum dispersion of the condition parameter can be used as an early diagnostic parameter. A possible screenplay of change of the stochastic process dispersion which describes the fluctuation parameter has been analyzed. Discusses an example of testing an organizing real manufacturing system according to the statistical characteristics of the condition parameter. Provides a general procedure of preventive maintenance of the system according to the condition, based on synthesis of global and local diagnostics according to a fluctuation thermodynamic model.

Deals with sources of fuzziness in individual parts and stages of maintenance and utilization of vehicles in large fleets. Effective vehicle fleet maintenance management requires data on reliability and maintainability. In most cases, however, available data are insuffucient for proper statistical identification of performance, making it impossible to define an adequate model to support decision‐making procedures. Demonstrates that, in such cases, the fuzzy set theory is convenient for modelling these processes so as to enable decision making and maintenance management.