Frontiers of Earth Science

In this paper we introduce the notion of explicit worst-case bounded adaptive algorithms for applications with fixed process-completion requirements. Such applications demand that a process be guaranteed to complete within an established time interval while adaptively reducing computational overhead during that interval, e.g., so as to reduce total energy usage. Our principal contribution is the Access-Adaptive Priority Search Tree (AAPST), which can provide efficient distribution-sensitive performance comparable to the splay tree, but do so within strict—and $$O(\log n)$$ optimal—worst-case per-query bounds. More specifically, while the splay tree is conjectured to offer optimal adaptive amortized query complexity, it may require O(n) for individual queries, whereas the AAPST offers competitive distribution-sensitive performance with strict $$O(\log n)$$ time complexity. This makes the AAPST more suitable for certain interactive (e.g., online and real-time) applications such as space system modules with reliability constraints involving rigid process-completion time intervals with secondary energy-minimization incentives.

Code smells have an adverse impact on the quality of source code. Martin Fowler initially identified a set of 22 code smells. Since the term "code smell", there have been multiple attempts to understand them through their detection and to discover relationships between them using correlation and other approaches. The literature demonstrates multiple studies in which code smells have been found to exhibit relationships with other code smells. Nevertheless, the temporary field is one of the 22 code smells that has not been analysed to determine its relationship with other code smells. It is important to consider temporary field, as it has a detrimental impact on the maintainability of the source code. The study has conducted a review of the 7 smell relations identified by Pietrzak and Walter and proposed 3 new smell relations. It has evaluated these smell relations between temporary field and 17 design smells in 10 popular open-source Java applications that are widely cited in the literature and publicly accessible. The study has also done a correlation analysis of temporary field with 17 design smells. All code smells in the study were detected using an open-source tool called "TFfinder". The study reveals 18 significant smell relations between temporary field and design smells. Utilization of smell relations can facilitate an in-depth comprehension of code smells and aid in the prioritization of code smells for refactoring purposes. In addition, it can assist a developer in identifying classes that need more maintenance effort and impact the maintainability of the code.

Integration of Software Product Line (SPL) and Agile Software Development (ASD) results in a new direction called Agile Product Line Engineering (APLE). Even though some studies in the literature have suggested efficient methods for integrating ASD and SPL, they have not yet addressed every facet of APLE’s characteristics, and these methods hardly ever take into account the SPL configuration process or the reuse of software resources when developing new products or expanding existing product lines. Despite extensive research efforts, a unified and holistic APLE methodology that integrates agile concepts across both Application Engineering (AE) and Domain Engineering (DE) phases remains elusive. Given this, we suggest a new APLE methodology to integrate ASD and SPL more effectively. The suggested approach iteratively builds the product line, and the system architecture grows over time. We have outlined a new variability mechanism called Variability on DemAnd (VODA) to boost the SPL configuration process. We performed the two-phased evaluation. (1) In the first phase, we considered empirical investigation to validate the proposed APLE methodology. We performed a randomized experiment to compare the proposed approach to a traditional system that typically applies agile principles within a proactive SPL but lacks agile-based variability mechanisms, dynamic product line architectures, and robust feedback. (2) In second phase, the proposed algorithm is tested for efficiency, performance, and effectiveness. We conduct the experiments to evaluate the proposed process (VODA) and obtained results are evaluated with Precision, Recall, Accuracy, and F-Measure. The findings indicate that the suggested approach offers benefits such as adaptable demand management, improved software resource reuse, lower configuration costs, and a shorter time to market. The second step (i.e. second phase of evaluation) results confirm the effectiveness of the proposed algorithm. The average precision value is 0.887, the average Recall value is 0.884 and the average F-Measure value is 0.878%.

For the consolidated management and supervising of massive networks, software-defined networking (SDN) is seen to be the best option. Nonetheless, it should be highlighted that SDN design experiences the same security problems as conventional networks. To bridge this gap, an efficient model for anomaly detection (AD) in SDN named Multi-verse Deer Hunting Optimization (MVDHO) is introduced. Firstly, SDN nodes are simulated. After that, SDN switches are controlled by the control plane to identify the condition of switches like ON, IDLE, or OFF conditions based on the detection plane. Secondly, the detection plane module consists of two modules, such traffic flow detection and AD. In the detection plane, the SDN switch flow rate is recorded in the form of time-series data and the condition of the switch is predicted based on time-series data using Deep Long short-term memory (LSTM). Similarly, in AD, the behaviour of the communication is recorded as a log file by extracting the significant features. Moreover, appropriate features are selected by mutual information. Finally, the detection of anomaly is performed employing Deep Q-Network, which is trained using MVDHO. Here, MVDHO is obtained by the combination of a Multi-verse Optimizer (MVO) and Deer Hunting Optimization Algorithm (DHOA). The detected anomalies are Denial of Service (DoS), Buffer_overflow, Guess_password, SQL attack and Named attack. The metrics utilized in this research namely, Traffic flow detection accuracy (TFDA), accuracy, true positive rate (TPR), and true negative rate (TNR) attained maximum values with 91.6%, 94.7%, 90.8%, and 86.5%, and also, the minimum value of computational time is 52.99s.

AbstractIt is vital to have precise specifications and verification of UML class diagrams to ensure the correctness of complex software systems. However, current specification and verification methods often face a challenge known as the frame problem. This problem occurs due to incomplete operation specifications that can lead to unintended system behavior. To tackle this issue, we have developed an automated solution to autonomously identify and define frame conditions, effectively minimizing the frame problem’s impact on class diagram verification. Frame conditions are explicit contracts that meticulously outline the permissible effects of operations within the system. Our approach carefully analyzes the behavioral blueprint of a class diagram and extracts crucial information to create these conditions. Through rigorous evaluations encompassing diverse UML diagrams and simulated execution scenarios, we have demonstrated the effectiveness of our approach in preventing unintended system behavior caused by the frame problem. We have integrated the approach into the Temporal Property Validator tool, empowering practitioners to leverage its benefits for practical class diagram specification and verification.

Fuzzy clustering is an unsupervised technique in which an object belongs to more than one cluster. In this paper, we have implemented and compared eight fuzzy clustering algorithms, FCM, IFCM, KFCM, and KIFCM with the Euclidean distance metric and same algorithms with the weighted mean distance metric, i.e., FCM-ϭ, IFCM-ϭ, KFCM-ϭ, and KIFCM-ϭ. None of the previous reviews in the literature have assessed the effectiveness of these algorithms on linearly and nonlinearly separable data. So, in this comparative analysis, we are focusing on data separability, also considering other factors such as noise-free and noisy data, the presence, and absence of outliers (if any), as well as clusters of varied size, shape, and density. We have conducted the experiment on twelve 2-D synthetic datasets and five real datasets from the UCI repository. It is observed that for linearly separable data, KIFCM and IFCM-ϭ perform considerably better in the presence of noise and outliers whereas for non-linearly separable data, KFCM, KFCM-ϭ, KIFCM, and KIFCM-ϭ perform better.

AbstractWhile AI is extensively transforming Software Engineering (SE) fields, SE is still in need of a framework to consider overall all phases to facilitate Automated Software Evolution (ASEv), particularly for intelligent applications that are context-rich instead of conquering each division independently. Its complexity comes from the intricacy of the intelligent applications, the heterogeneity of the data sources, and the constant changes in the context. This study proposes a conceptual framework for achieving automated software evolution, emphasizing the importance of multimodality learning. A Selective Sequential Scope Model (3 S) model is developed based on the conceptual framework, and it can be used to categorize existing and future research when it covers different SE phases and multimodal learning tasks. This research is a preliminary step toward the blueprint of a higher-level ASEv. The proposed conceptual framework can act as a practical guideline for practitioners to prepare themselves for diving into this area. Although the study is about intelligent applications, the framework and analysis methods may be adapted for other types of software as AI brings more intelligence into their life cycles.

The operation of hybrid microgrid systems without sufficient control can result in system synchronization issues within renewable energy sources (RESs) and the power network. This impact on the synchronization can drastically affect the whole equilibrium, stability, and reliability. Hence, to eliminate such issues, this paper has focussed on the development of a novel, improved fire hawk optimization technique (IFHOT) for preparing three ANFIS regulators for a stable energy management system of a utility network-connected hybrid non-conventional system. The hybrid system features the combination of a battery unit + wind power generator (W) + photovoltaic (PV) array + fuel cell (FC) stack. The IFHOT-trained ANFIS-based monitoring control system determines the switching of the various RESs, thereby regulating the power they generate. Load power requirement, grid power need, and condition of the battery, also known as SOC, are considered for the operation of the ANFIS-based management system. The research work carried out here aims to optimize the switching and power generation of the various RESs. It also ensures fast instability mitigation that can be observed from the voltage, current, and power graphs. Furthermore, successful comparisons with other intelligent controllers, such as fire hawk optimization technique (FHOT) tuned ANFIS and circle search algorithm (CSA) tuned ANFIS, are executed in the proposed work. Concerning voltage, current, power control and harmonics alleviation, IFHOT-tuned ANFIS has yielded superior results compared to FHOT-tuned ANFIS and CSA-tuned ANFIS. All the experiments have been performed on the MATLAB/SIMULINK platform.

AbstractWorking with models can lead to inconsistencies, e.g., due to erroneous or contradictory actions during concurrent modeling processes. Modern modeling environments typically tolerate inconsistencies and support their detection. However, at a later stage of development, models are expected to be consistent, meaning their inconsistencies should be considered and resolved. The process of resolving model inconsistencies is commonly referred to as model repair. Our approach to model repair is semi-automatic in the sense that the repair tool computes appropriate repair plans and the modeler decides which path to take. The speciality of our approach is that the repair process can register any small improvement in the model. This allows the interaction with the user to be optimized, resulting in an approach with a high level of automation on the one hand and flexible configuration options on the other. The approach focuses on providing repair plans that do not have side effects, i.e., the computed repair plans do not inadvertently introduce a new inconsistency of already repaired constraints into the model. Since models often have a graph-like structure, we present our approach to model repair based on graphs. Our approach is completely formal—we use the algebraic graph transformation approach to prove its correctness. We also present a prototype implementation of our repair approach based on the Eclipse Modeling Framework and Henshin, a model transformation engine based on graph transformation, to perform the actual model repair. A first performance evaluation shows that graphs with up to 1000 nodes can be repaired in about 10 s.

Accurate and efficient classification of software requirements is crucial for the success of system, as functional and nonfunctional requirements define the fundamental characteristics and constraints of a system. Documenting software requirements in natural language can lead to uncertainties such as ambiguity, inconsistency, or poor readability. Additionally, manual extraction of requirements can be tedious and prone to errors due to the need for precise interpretation, which increases the risk of miscommunication and mistakes during the development process. So, it is vital to use effective techniques like natural language processing to clearly understand the requirements. This paper proposes the use of deep learning models in conjunction with natural language processing, followed by flower pollination optimizer, to automate the classification task. The methodology leverages natural language processing to extract meaningful features used to train a convolutional neural network model. The convolution neural network model is enhanced using the flower pollination optimizer algorithm to ensure better convergence. The approach is implemented using an industry SmartNet dataset. To tackle the challenge of class imbalance, the synthetic minority oversampling technique is used. The approach is validated on both balanced and unbalanced datasets to demonstrate its effectiveness. Results show that the CNN-FPO framework performs exceptionally well, with accuracies ranging between 94.48% and 97.13% for balanced and 87.45% to 98% for unbalanced dataset.

This research proposes a novel time-dependent interval-valued function-based multi-objective reliability redundancy allocation problem (TIVF-MORRAP) focused on multi-stage series–parallel systems. Time is a critical factor in assessing system reliability and cost. The novel contribution of this study is the use of an interval-valued function (IVF) approach to manage uncertainties in component reliability, cost, and repair costs, with time as a key variable. The objective is to boost system reliability and minimize costs over time by efficiently allocating redundant components at each stage. The process ensures a restricted allocation of duplicates across all stages and the entire system. In this problem the reduction in component reliability and cost are modeled by the varying radius length along the inverse logarithmic spiral over time. Likewise, the escalation in component repair costs is depicted by the logarithmic spiral. In this study, NSGA-II-AGDV is introduced, a multi-objective evolutionary algorithm (MOEA) that combines NSGA-II (Non-dominated Sorting Genetic Algorithm-II) with agglomerative and divisive clustering algorithms and the Topsis method to solve the problem. Unlike NSGA-II, which utilizes crowding distance, many researchers have adopted a single clustering technique to improve diversity and limit the solution set size. The proposed algorithm integrates two clustering techniques, enhancing functionality while also reducing execution time. A benchmark problem verifies the proposed method, showing enhanced performance and better convergence to true Pareto optimal solutions compared to NSGA-II and NSGA-II with crowding distance elimination (NSGA-II-CDE) across various time values.

Due to their predominant use of natural language, requirements are prone to defects like inconsistency and incompleteness. Consequently, quality assurance processes are commonly applied to requirements manually. However, manual execution of these processes can be laborious and may inadvertently overlook critical quality issues due to time and budget constraints. This paper introduces ARIA, an innovative question–answering (QA) approach designed to automate support for stakeholders, including requirements engineers, during the analysis of NL requirements. The ability to ask questions and get immediate answers is extremely useful in many quality assurance situations, especially for incompleteness detection. The challenge of automating the answering of requirements-related questions is considerable, given the potential scope of the search for answers extending beyond the provided requirements specification. To overcome this challenge, ARIA integrates support for mining external domain knowledge resources like internet search results. Evaluation of seven diverse use cases drawn from the PURE dataset demonstrates ARIA’s robustness and applicability across a range of real-life scenarios, highlighting its potential to significantly improve the quality and effectiveness of requirements analysis processes. This work represents one of the initial endeavors to seamlessly blend QA and external domain knowledge, effectively addressing complexities in requirements engineering through a flexible framework designed to readily integrate with evolving, advanced-level Generative LLMs.

Ornithology, the study of birds, plays a crucial role in understanding the complex interplay between avian species and their ecosystems, offering insights into biodiversity and global ecological systems. In recent years, the conservation of bird species has become increasingly critical, particularly as habitat degradation poses a significant threat to endangered species. Accurate identification and classification of bird species are essential components of conservation strategies. This research presents a modified ResNet152v2 framework designed to enhance the precision and efficiency of bird species classification. By leveraging advanced deep learning techniques, this framework aims to support conservation efforts by facilitating the accurate monitoring and protection of diverse avian populations. This study introduces a transfer learning approach for bird species identification using a modified ResNet152v2 framework, leveraging a publicly available dataset of 87,874 images spanning 515 bird species. The model achieved an impressive test accuracy of 97.67% and a categorical accuracy of 98.81%. A key contribution lies in the utilization of principal component analysis (PCA) for dimensionality reduction, optimizing the feature vector by 15%. Additionally, root mean squared propagation (RMSProp) was employed to enhance the feature vector. L2 regularization and dropout at the input and the subsequent layer were introduced to mitigate overfitting. The model underwent rigorous training, resulting in a minimum training loss of 0.0397 and a validation loss of 0.0044. The achieved average precision (98.66%), recall (97.73%), and F1-score (98.2%) values further validate the model’s effectiveness. This study presents a novel combination of techniques, emphasizing the integration of transfer learning, dimensionality reduction, and dropout for improved accuracy, resulting in a robust model with potential applications in ecological research and environmental monitoring.