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Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks. © 2022 The Authors, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access