Institutional Repository of Academic Research University of Macedonia
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2215 research outputs found
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Merging Smell Detectors: Evidence on the Agreement of Multiple Tools
No full textTechnical Debt estimation relies heavily on the use of static anal-ysis tools looking for violations of pre-defined rules. Largely, Technical Debt principal is attributed to the presence of low-level code smells, unavoidably tying the effort for fixing the problems with mere coding inefficiencies. At the same time, despite their simple definition, the detection of most code smells is non-trivial and subjective, rendering the assessment of Technical Debt prin-cipal dubious. To this end, we have revisited the literature on code smell detection approaches backed by tools and developed an Eclipse plugin that incorporates six code smell detection ap-proaches. The combined application of various smell detectors can increase the certainty of identifying actual code smells that matter to the development team. We also conduct a case study to investigate the agreement among the employed code smell detec-tors. To our surprise the level of agreement is quite low even for relatively simple code smells, threating the validity of existing TD analysis tools and calling for increased attention to the precise specification of code and design level issues. Source code: https://github.com/apostolisich/SmellDetectorMerger6165Proceedings of the International Conference on Technical Deb
Lean Digital Culture as an Enabler of Corporate Sustainability Performance: The Mediating Role of Intention to Use Industry 4.0 Technologies
No full textCorporate sustainability considers the long-term creation of value and the ability of companies to survive and thrive, integrating the environmental and social aspects of business activities. In this sense, the adoption of lean management principles and the promotion of a digital-friendly culture are highlighted in this study as key pathways to sustainability, mainly because they are linked to continuous improvement and the seamless integration of new technologies to increase efficiency and achieve strategic business goals and, ultimately, to satisfy stakeholders. Therefore, assessing employees’ perceptions of these two important cultural dimensions: the lean and the digital, is expected to provide management with important information on their level of integration of Industry 4.0 technologies and help diagnose any drawbacks, motives, and opportunities to promote the adaptation of new technologies. In this line of thought, this study proposes a conceptual framework that links the lean and digital dimensions of organisational culture with employees’ intention to use Industry 4.0 technologies. The effect of integrating digital technologies into corporate culture is, in turn, expected to improve sustainability performance. Therefore, the purpose of this study is to present an integrated research model to assess employees’ lean and digital culture, their level of intention to use Industry 4.0 technologies, and the overall impact on corporate sustainability. The proposed model will then be empirically validated through a field study. The empirical results aim to highlight the people-related enablers and drawbacks of digital transformation by lending a human perspective on the adoption of Industry 4.0 technologies, thus facilitating a smooth transition to an Industry 5.0 mindset.5874Embracing Sustainability Management Through Excellence in Service
Supporting single responsibility through automated extract method refactoring
No full textThe responsibility of a method/function is to perform some desired computations and disseminate the results to its caller through various deliverables, including object fields and variables in output instructions. Based on this definition of responsibility, this paper offers a new algorithm to refactor long methods to those with a single responsibility. We propose a backward slicing algorithm to decompose a long method into slightly overlapping slices. The slices are computed for each output instruction, representing the outcome of a responsibility delegated to the method. The slices will be non-overlapping if the slicing criteria address the same output variable. The slices are further extracted as independent methods, invoked by the original method, if certain behavioral preservation are made. The proposed method has been evaluated on the GEMS extract method refactoring benchmark and three real-world projects. On average, our experiments demonstrate at least a 29.6% improvement in precision and a 12.1% improvement in the recall of uncovering refactoring opportunities compared to the state-of-the-art approaches. Furthermore, our tool improves method-level cohesion metrics by an average of 20% after refactoring. Experimental results confirm the applicability of the proposed approach in extracting methods with a single responsibility.2912
Optimized AI-Driven Semantic Web Approach for Enhancing Phishing Detection in E-Commerce Platforms
No full textFor e-commerce systems, phishing attempts remain a major threat, so sophisticated detection techniques using Semantic Web and artificial intelligence are very necessary. An efficient AI-driven Semantic Web method for phishing detection enhancement is presented in this work. The approach uses the Chi-square feature selection approach along with the Adaptive Differential Evolution with Optional External Archive (JADE) algorithm to optimize the hyperparameters of a Convolutional Neural Network (CNN) model. Having grown up on a large collection of more than 11,000 webpages, the model attained 93% accuracy. Although alternative models sometimes exceeded it in accuracy, the suggested method always showed the lowest loss values throughout all epochs, therefore stressing its stability and efficiency. Comparative study using conventional models confirms its resilience against phishing attacks for protecting e-commerce systems.20111
A Machine Learning Approach For The Identification Of Olive Fruit Fly in Greece
No full textContemporary agriculture faces critical challenges to maintain a future that meets global food demand. Precise and early detection of plantations' pest and disease threats is crucial for controlling their spread, maintaining production quality and volume, minimizing costs, and reducing trade disruptions, sometimes even lessening human health risks. Pest management in agriculture benefits significantly from the application of deep learning (DL) techniques for more efficient detection and monitoring, overcoming the inefficiencies of traditional labor-intensive methods. This study develops a convolutional neural network (CNN) and benchmarks it against state-of-the-art (SOTA) DL models to identify the primary threat to olive trees, Bactrocera oleae (also known as Dacus). Using a data set composed of images that span 102 insect categories, CNN demonstrated a high accuracy of 96. 32% to distinguish Dacus from other insect species.58622024 9th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM
Business Process Redesign: A Systematic Review of Evaluation Approaches Prior to Implementation
No full textThe continuous and systematic redesign of key business processes is very important for businesses and organizations that seek to achieve cost savings and efficiency enhancements. Selecting the most impactful processes and ensuring a successful redesign initiative remains an important topic that motivated the authors to conduct a Systematic Literature Review (SLR) on Business Process Redesign (BPR) Evaluation methodologies by applying an established eight-step SLR guide. The review sheds light on the current state of research and highlights the research gap by considering two dimensions of BPR artifacts: (a) the type of evaluation and (b) the generalizability of the existing approaches. The findings indicate that there is a lack of systematic methodologies in literature that properly evaluate the redesign capacity of models prior to implementation. Additionally, the existing methodologies do not cumulatively evaluate the quality characteristics that are necessary for BPR implementation or the applicability of BPR heuristics, and do not bear the generalizability to be readily used in a more general context. This paper aims to provide researchers with the necessary context and motivation to bridge this gap and further systematize BPR methodologies that can preselect the most suitable business processes for redesign.71799
A variable neighborhood search approach for solving a real-world hierarchical multi-echelon vehicle routing problem involving HCT vehicles
No full textThis paper studies the Hierarchical Multi-Switch Multi-Echelon VRP (HMSME-VRP), a newly introduced VRP variant based on a real-world case involving High Capacity Vehicles (HCV). The problem originates from the policies of a distribution company in the Nordic countries where HCVs of up to 34.5 m and up to 76 tons are allowed. The HMSME-VRP offer a new way to model distribution problems to cover large geographical areas without substantial costs in infrastructure. Furthermore, it adds complexity to the standard VRP and, as such, remains NP-hard and difficult to solve to optimality. Indeed, it has been demonstrated that only very small instances can be solved to optimality by a commercial solver. Thus, in order to handle instances of real-world size, we propose two General Variable Neighborhood Search (GVNS) procedures, the second of which is adaptive, utilizing an intelligent reordering mechanism. In order to evaluate the proposed procedures, 48 benchmark instances of various sizes and characteristics are generated and made publicly available, comprising of clustered, random, and semi-clustered customers. The computational results show that both GVNS procedures outperform the exact solver. Additionally, the adaptive version outperforms the conventional version based on both average and best solutions. Furthermore, we present a statistical analysis to verify the superiority of the adaptive version.16510659410660
Analyzing concentration in the Greek public procurement market: a network theory approach
No full textThis study uses network theory to analyze the structure and concentration of the Greek public procurement market, focusing on the relationships between Contracting Authorities (CAs) and Economic Operators (EOs) within different Common Procurement Vocabulary (CPV) groups. By examining degree distribution curves and identifying dominant economic operators, we gain valuable insights into market dynamics and competition in Greece. The findings reveal the presence of market concentration, where a few EOs receive a disproportionate share of contracts within certain CPV groups which suggests potential market dominance and lack of competition. In contrast, some CPV groups demonstrate a more balanced distribution of contracts among EOs, suggesting a healthier competitive environment. The analysis of degree distributions between sub-networks based on CPV groups indicates variations in market structures between sectors. These differences highlight the heterogeneity in the Greek public procurement market, as well as the need for sector-specific policy interventions. Given that the concentration of contract awards raises competition, fairness and transparency concerns, the implications of the findings are important for policymakers, regulators and stakeholders involved in the Greek public procurement market. While this study provides valuable information, limitations including variations in data availability and potential inaccuracies in recorded information exist. Future research should address these limitations and explore the effects of market concentration on CPV codes in greater depth. This study contributes to the public procurement literature and serves as a basis for further research and policy making in the Greek procurement sector.43148
Autonomous Vehicle Routing Optimization: A Survey
No full textIndustry 4.0 era is characterized by several technological advances, such as the technology of Autonomous Vehicles, which is expected to increase mobility efficiency. A critical component in the autonomous vehicle navigation process is path planning. To this end, the present survey focused on the investigation of research contributions on the optimal path/routes scheduling of autonomous vehicles. The main objective of the conducted review is the classification of relative research articles, according to their optimization criteria, optimization models, and optimization methods.21412313
Factors That Affect the Acceptance of Educational AI Tools by Greek Teachers—A Structural Equation Modelling Study
No full textThe discussion around integrating AI technologies into educational practice is current among scholars and in sociopolitical circles. This study examines the factors influencing teachers’ acceptance of educational AI tool (EAIT) use, aiming to inform the development of a pedagogical framework for the responsible integration of AI tools in education. A conceptual model was developed by amalgamating constructs of TAM (perceived usefulness and perceived ease of use) and UTAUT (social influence and facilitating conditions) while integrating the variables of perceived trust and personal innovativeness and considering the impact of teachers’ pedagogical beliefs. A total of 342 Greek teachers participated in the quantitative survey conducted. The proposed model was evaluated using partial least squares structural equation modelling (PLS-SEM). The findings illuminated perceived usefulness as the most significant predictor of teachers’ behavioural intention to use EAIT. The research also revealed that social influence and personal innovativeness exert considerable influence. While constructivist pedagogical beliefs were found to have no direct impact on EAIT acceptance, the results indicated that educators who embrace those teaching methods exhibit a high propensity to perceive EAIT as useful and trustworthy. Furthermore, the study’s analysis demonstrated that trust had a significantly positive effect on usefulness, and innovativeness influences positively and significantly both usefulness and ease of use.1492560257