Improving design quality using meta‐pattern transformations: a metric‐based approach

This study investigates the integration of AI-powered tools into accessibility design education for visual communication students. The aim is to assess how AI can enhance students' understanding of accessibility principles, improve the quality of their design work, and increase engagement with the learning process. The study involved a curriculum incorporating AI tools to evaluate key accessibility factors such as contrast ratios, readability, and screen reader compatibility. Pretest and posttest assessments, design project evaluations, and student surveys were used to measure knowledge improvement, design quality, and engagement. The results indicate a significant 32.7% improvement in students' knowledge of accessibility principles and a 24.8-point increase in design accessibility scores. Additionally, student engagement significantly improved, reflecting high satisfaction with the AI tools. Regression analysis revealed a positive correlation between the time spent using AI tools and the improvement in design quality. These findings suggest that AI-powered tools can effectively support the development of accessibility skills in design education, providing students with real-time feedback and fostering a more interactive and inclusive learning experience. This study contributes to the growing body of research on the application of AI in design education and highlights the potential of AI tools to transform accessibility education. The findings underscore the importance of preparing future designers to create more accessible and inclusive digital content, with AI playing a pivotal role in this process.

As a part of the company's digitalization project, the Well Delivery Process (WDP) software solution was successfully developed and launched in early 2021. By using this solution, engineers benefit from the project objectives like process standardization, well design quality, and well planning cycle time reduction. The WDP software consists of a series of integrated well delivery workflows including well design, drilling execution monitoring, and closeout reports within a single cloud-based platform. The system also provides users with many automation features such as well design rules validation, offset well analysis, risk analysis, automated calculations, and digital reports (drilling programs). Through this paper, we share our experiences and observations on the system development and implementation, and also challenges and limitations that we faced to roll it out successfully. In our conventional practices before the digital transformation, drilling engineers had their individual practices/formats to perform the well delivery process using various applications and techniques. Computer applications were used to generate and compile a drilling program, but there was no standard way of performing this process. In situations where the design parameters and/or subsurface information change, engineers should have repeated the entire process to revise the design and/or the drilling program manually. To standardize the process, minimize repetitive tasks, and provide engineers with all required digital tools, the first solution (a prototype computer program) was developed in-house to prove the concept. After multiple peer reviews and trials, we used the prototype program to specify user requirements for generating the WDP software solution. An agile methodology was utilized to engage all drilling engineers in each sprint of the WDP development phase to ensure that their requirements are fulfilled. Upon completion of the development phase, a comprehensive User Acceptance Test (UAT) was conducted followed by training sessions before the cut-off date for switching from the manual process to the WDP digital workflow. Switching from existing practice to a new way of work is always challenging especially when it comes to introducing new software. There was some resistance at the start-up period due to users' unfamiliarity with the software and also software bugs that were not found during testing periods and UAT. A support team was set up to ensure immediate action on users' requirements and any technical issues with the software. During the challenging transition period, all users were fully supported until make sure they are enough familiar with the system to complete the task effectively. The support team deployed hotfixes (bug fixing) as quickly as possible with no major impact on the drilling programs' timeline, monitored the system performance on daily basis, and gathered feedback for further improvements. At present, the WDP is the primary system used by all drilling engineers in the company's domestic assets as an integrated end-to-end well delivery workflow that provides an improved design quality in a shorter planning time. More than 200 well design workflows have been initiated and approved through the system since it is being used. Based on the recent users' satisfaction survey and feedback, an overall rating of 4.2 from 5 shows the WDP solution's effectiveness with many popular and useful features especially offset well analyzer and well design dashboards. As one of the first end-to-end workflow generations in the drilling engineering process, the WDP could save the working time up to 35-40% (voted by users) through a centralized dataset platform and its holistic approach to process standardization/automation. Besides design quality and cycle time improvement, the system also enables advanced data analytics for further automation and enhancements.

Software restructuring is the process of re-organizing the logical structure of existing software systems in order to improve particular quality attributes of software products. During design or maintenance, software developers often use intuition, rather than an objective set of criteria, to determine, recapture, or restructure the design of a software system. In this dissertation, we provide a quantitative framework for software restructuring. In the framework, restructuring decisions are guided by visualized design information and quantitative design-level measures. The restructuring process is automated by supporting tools. A set of design-level cohesion and coupling measures provides the objective criteria for comparing alternative design structures. They are derived, compared, and applied in our software restructuring framework. The process insures that restructuring results in measurable and visible improvements in design quality. Applying our framework can result in restructured software systems that are easier to maintain and reuse.

Background: Implementation of Building Information Modelling (BIM) has proven to deliver major performance improvements in Program efficiency, design quality, constructability, waste reduction, environmental performance, and capital & operational cost management of built environment projects. Yet BIM implementation in low BIM maturity markets, such as the UAE, is limited to technology applications which fail to deliver the full potential of BIM benefits to client originations. Objective: The purpose of this paper is to investigate BIM implementation from a client’s perspective and to present a case study that exhibits a level 2 BIM implementation process in a traditional procurement environment. Methods: The study has used a case study approach combined with a literature review. A critical appraisal of relevant literature is presented to highlight key issues hindering BIM implementation for client organizations, especially in developing BIM markets, such as the UAE. The research is collected using an action research approach within a case study, including project document audit, participation in project collaboration meetings and extensive communication with the project stakeholders. The case study is presented in a practice-oriented research format describing the project details, procurement approach, BIM development & management process and benefits achieved for the project client. Results: The paper presents a structured approach to strategically introduce BIM within a low BIM maturity market, creating partnering relationships, empower the supply chain partners and achieving significant BIM benefits with minimum disruption to existing work practices. The paper highlights that although BIM requires a step-change in the work practices of the construction industry, yet it is possible to successfully implement BIM with traditional procurement settings, which may be a critical feature in a certain market or a client requirement. Conclusion: There is a need for case study based, practice-oriented research work within the domain of BIM implementation. Construction clients in low maturity BIM markets are concerned about the perceived benefits of BIM and its practical implementation within existing business practices, which is addressed in this paper. Overall, the findings of this study are useful for construction industry clients and academia in redefining the existing work practices to incorporate BIM-enabled processes and applications.

Software engineers often need to identify and correct design defects, i.e., recurring design problems that hinder development and maintenance by making programs harder to comprehend and/or evolve. While detection of design defects is an actively researched area, their correction — mainly a manual and time-consuming activity — is yet to be extensively investigated for automation. In this paper, we propose an automated approach for suggesting defect-correcting refactorings using relational concept analysis (rca). The added value of rca consists in exploiting the links between formal objects which abound in a software re-engineering context. We validated our approach on instances of the Blob design defect taken from four different open-source programs.KeywordsDesign DefectsRefactoringRelational Concept Analysis

Design defects come from poor design choices and have the effect of degrading the quality of object-oriented designs.Therefore, they present opportunities for improvements.However, design defects have not been precisely specified and there are few appropriate tools that allow their detection as well as their correction. Our goal is to provide a systematic method to specify systematically design defects precisely and to generate automatically detection and correction algorithms from their specifications. The detection algorithms are based not only on metrics but also on semantical and structural properties whereas the correction algorithms are based on refactorings. We apply and validate these algorithms on open-source object-oriented programs to show that our method allows a systematic specification, a precise detection, and a suitable correction of design defects.

As the size and complexity of service oriented applications increases ensuring the quality of design of services that constitute these applications is becoming critical. Poor design of services results in unnecessarily complex and inflexible applications that are difficult to maintain and evolve. Service design has been the subject of intense research interest for almost a decade and there is a wide agreement about the key service design principles that promote maintainability of software services. Recent research efforts include attempts to develop reliable metrics for assessing design quality of service-oriented applications. Most of these metrics were adapted from metrics for object-oriented software and focus on measuring intra-service cohesion and inter-service coupling. In this paper we argue that such metrics are of limited use in assessing the quality of coarse-grained document-centric services used in majority of SOA applications and propose a Message Data Coupling Index (MDCI) - a metric that evaluates orthogonality of a family of XML schemas based on the level of data coupling. We describe the implementation of a prototype tool that computes several variants of the MDCI metric.

This study presents a virtual acoustic characterization of a multipurpose sports facility, focusing on a case study of the coliseum at “Unidad Educativa Municipal Sebastián de Benalcázar” in Quito, Ecuador. Utilizing a non-intrusive, simulation-based approach, an initial detailed survey of the coliseum’s interior was conducted. AutoCAD and SketchUp software were utilized to generate a three-dimensional (3D) model based on the acquired data. This model was then exported to ODEON software for acoustic simulation. Important acoustic parameters, including reverberation duration, clarity, STI (Speech Transmission Index), and RASTI (Rapid Speech Transmission Index), were obtained from the simulation. These parameters were instrumental in characterizing the sports facility’s acoustic quality and comparing it with established reference values. Special emphasis was given to the initial parameters of reverberation time and STI, crucial for assessing the acoustic quality of the space. This virtual characterization provides significant insights into the acoustical properties of the coliseum, highlighting its potential and limitations as a multifunctional sports venue. The present study makes a significant contribution to the field of virtual acoustic assessments by providing a comprehensive framework for future improvements in the acoustic design and sound quality of similar environments. The study specifically addresses the distinct geographical and architectural context of Quito, Ecuador.

The present study investigates the application of digital diagnostic tools in evaluating the communicative and organizational competencies of medical students within contemporary medical education frameworks. The research emphasizes the integration of advanced information technologies to provide an objective, comprehensive, and continuous assessment of students’ soft skills, which are critical for clinical performance, interprofessional collaboration, and patient-centered care. By leveraging digital platforms, the study explores innovative methods for monitoring, analyzing, and enhancing students’ abilities to communicate effectively, manage tasks efficiently, and demonstrate leadership in healthcare settings. The findings suggest that the systematic use of digital diagnostic tools not only supports personalized learning trajectories but also contributes to evidence-based improvements in curriculum design and educational quality in medical schools.

Previous epidemiologic studies have suggested that exposure to electric or magnetic fields in occupational and residential environments may cause cancer. Recent experimental findings provide some support for the hypothesis that exposure to extremely low-frequency electromagnetic fields reduces the pineal gland's nocturnal production of the hormone melatonin, thereby increasing susceptibility to sex hormone-related cancers such as breast cancer. Our purpose was to assess the evidence that cancer of the female breast might be associated with exposure to extremely low-frequency electromagnetic fields. Records of women who had breast cancer as the underlying cause of their death (ICD-9 174) and control subjects (four per case) were selected from computer files of U.S. mortality data for the years 1985-1989. Women 20 years and older at the time of their death were eligible for inclusion if they were residents of and died in one of the 24 states that provided death certification records with occupation and industry codes to the National Center for Health Statistics for at least 1 year during the study interval. Data from death certificates were used to classify the case and control subjects with regard to potential occupational exposure to electric and magnetic fields. Control subjects were a random sample of women who died of any other underlying cause, excluding leukemia and brain cancer. The data analysis contrasted 68 women with breast cancer and 199 controls, all with electrical occupations, with 27,814 women with breast cancer and 110,750 controls, all of whom had other occupations. Electrical workers had excess mortality from breast cancer relative to other employed women [odds ratio (OR) = 1.38; 95% confidence interval (CI) = 1.04-1.82]). Adjusted ORs for specific electrical occupations were 1.73 (95% CI = 0.92-3.25) for electrical engineers, 1.28 (95% CI = 0.79-2.07) for electrical technicians, and 2.17 (95% CI = 1.17-4.02) for telephone installers, repairers, and line workers. There was no excess of breast cancer, however, in seven other occupations held more frequently by women and also involving potentially elevated electrical exposures, including telephone operators, data keyers, and computer operators and programmers. In light of the limitations inherent in death certification data and the design of this study, any conclusions regarding the hypothesis that exposure to extremely low-frequency electromagnetic fields causes breast cancer among women must be limited. Nevertheless, our findings are broadly consistent with that hypothesis and encourage further investigation with improvements in study design and data quality.

Ensuring Performance by Geometric Quality Control and Specifications for Parabolic Trough Solar Fields

Urban design in central Sydney 1945–2002: Laissez-Faire and discretionary traditions in the accidental city

An alternative approach for measuring object-oriented (OO) software quality has been proposed by applying the Analytic Hierarchy Process (AHP) scheme. This approach is effective in defining the rank of software quality over a number of Java applications based on two sets of metric values representing main OO structural properties. The Metrics for Object-Oriented Software Engineering (MOOSE) and Metrics for Object-Oriented Design (MOOD) that have been describing individual characteristic of OO property now are integrated by means of AHPs pairwise comparison to deliver an estimate quantitative value of OO software design quality. A series of experiments were conducted by applying the approach to students works in Fundamental of Programming class. The results of applying AHP scheme were compared to experts judgments for verification. The result demonstrates the solidity of AHP mechanism for the intended purpose of measuring OO design quality.

Background: Automatic static analysis (ASA) tools examine source code to discover issues, i.e. code patterns that are symptoms of bad programming practices and that can lead to defective behavior. Studies in the literature have shown that these tools find defects earlier than other verification activities, but they produce a substantial number of false positive warnings. For this reason, an alternative approach is to use the set of ASA issues to identify defect prone files and components rather than focusing on the individual issues. Aim: We conducted an exploratory study to investigate whether ASA issues can be used as early indicators of faulty files and components and, for the first time, whether they point to a decay of specific software quality attributes, such as maintainability or functionality. Our aim is to understand the critical parameters and feasibility of such an approach to feed into future research on more specific quality and defect prediction models. Method: We analyzed an industrial C# web application using the Resharper ASA tool and explored if significant correlations exist in such a data set. Results: We found promising results when predicting defect-prone files. A set of specific Resharper categories are better indicators of faulty files than common software metrics or the collection of issues of all issue categories, and these categories correlate to different software quality attributes. Conclusions: Our advice for future research is to perform analysis on file rather component level and to evaluate the generalizability of categories. We also recommend using larger datasets as we learned that data sparseness can lead to challenges in the proposed analysis process.

Software metrics are of great importance in object-oriented design assessment. They quantify various aspects of design entities and play an important role in predicting design quality. Despite the fact that software metrics have become increasingly useful, they raise several issues. Among them, relevant to our research are the issue of setting threshold values and the problem of measurement results interpretation. Fuzzy clustering analysis is used to overcome the limitations of the existing approaches that are using threshold values for metrics and to provide a better interpretation of the obtained measurement results. This paper focuses on metrics-based design flaw detection in object-oriented design. A new metric, Design Flaw Entropy which measures the distribution of design flaws into the analyzed system is introduced. To validate the proposed approach, a case study was also proposed.