Refactoring, a widely adopted technique, has proven effective in facilitating and reducing maintenance activities and costs. Nonetheless, the effects of applying refactoring techniques on software quality exhibit inconsistencies and contradictions, leading to conflicting evidence on their overall benefit. Consequently, software developers face challenges in leveraging these techniques to improve software quality. Moreover, the absence of a categorization model hampers developers' ability to decide the most suitable refactoring techniques for improving software quality, considering specific design goals. Thus, this study aims to propose a novel refactoring categorization model that categorizes techniques based on their measurable impacts on internal quality attributes. Initially, the most common refactoring techniques used by software practitioners were identified. Subsequently, an experimental study was conducted using five case studies to measure the impacts of refactoring techniques on internal quality attributes. A subsequent multi-case analysis was conducted to analyze these effects across the case studies. The proposed model was developed based on the experimental study results and the subsequent multi-case analysis. The model categorizes refactoring techniques into green, yellow, and red categories. The proposed model, by acting as a guideline, assists developers in understanding the effects of each refactoring technique on quality attributes, allowing them to select appropriate techniques to improve specific quality attributes. Compared to existing studies, the proposed model emerges superior by offering a more granular categorization (green, yellow, and red categories), and its range is wide (including ten refactoring techniques and eleven internal quality attributes). Such granularity not only equips developers with an in-depth understanding of each technique's impact but also fosters informed decision-making. In addition, the proposed model outperforms current studies and offers a more nuanced understanding, explicitly highlighting areas of strength and concern for each refactoring technique. This enhancement aids developers in better grasping the implications of each refactoring technique on quality attributes. As a result, the model simplifies the decision-making process for developers, saving time and effort that would otherwise be spent weighing the benefits and drawbacks of various refactoring techniques. Furthermore, it has the potential to help reduce maintenance activities and associated costs.
In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers. This study investigates the use of the MQTT protocol to enhance the performance of circularity measurement data transmission between cloud servers and round-hole data sources through Open CV. Accurate inspection of circular characteristics, particularly roundness errors, is vital for lubricant distribution, assemblies, and rotational force innovation. Circularity measurement techniques employ algorithms like the minimal zone circle tolerance algorithm. Vision inspection systems, utilizing image processing techniques, can promptly and accurately detect quality concerns by analyzing the model's surface through circular dimension analysis. This involves sending the model's image to a computer, which employs techniques such as Hough Transform, Edge Detection, and Contour Analysis to identify circular features and extract relevant parameters. This method is utilized in the camera industry and component assembly. To assess the performance, a comparative experiment was conducted between the non-contact-based 3SMVI system and the contact-based CMM system widely used in various industries for roundness evaluation. The CMM technique is known for its high precision but is time-consuming. Experimental results indicated a variation of 5 to 9.6 micrometers between the two methods. It is suggested that using a high-resolution camera and appropriate lighting conditions can further enhance result precision.