Accurate Software Research Articles

SOFTWARE COST EFFORT AND TIME ESTIMATION USING DRAGONFLY WHALE LION OPTIMIZED DEEP NEURAL NETWORK

Effective software development depends on the exact estimation of effort, time, cost, and customer satisfaction. Software project management requires an accurate evaluation of software development's effort, time, and cost, often underestimated or overestimated. So far, methodology has yet to accurately and reliably estimate the cost of software development. To overcome this issue, this paper proposed a constructive rapid application development model based on software cost effort and time estimation approach (CORADMO-based CETA) for accurate software cost estimation. The data requirements, cost drivers, constraints, and priorities are given as input to the fuzzy inference system (FIS). The processed output, such as effort, time, and cost for the nominal plan, shortest schedule plan, and least cost plan, is computed in the FIS. To reduce the effort, time, and cost, the output is optimized by dragonfly whale lion optimization (DWLO), which provides the best-estimated effort, time, and cost as an output for software development. The proposed CORADMO-based CETA model is tested in the NASA 93 dataset using MATLAB. The performance of the CORADMO-based CETA method is measured in terms of Pred (25 %), magnitude of relative error, and mean magnitude of relative error, attaining the values of 80.72 %, 87.94 %, and 98.13 %, respectively. Finally, the CORADMO-based CETA model justifies the suitability of dragonfly whale lion optimization with the proposed fuzzy logic.

Accuracy of imaging software usable in clinical settings for 3D rendering of tooth structures.

The aim of the present study was to evaluate the segmentation accuracy of the dentition by testing four open-source semi-automatic software programs. Twenty CBCT scans were selected to perform semi-automatic segmentation of the maxillary and mandibular dentition. The software programs tested were InVesalius, ITK-SNAP, 3D Slicer, and Seg3D. In addition, each tooth model was manually segmented using Mimics software; this was set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the tooth models obtained with the semi-automatic software and the GS model as well as to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated, calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the tooth models compared with the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed to perform comparisons between the investigated software programs. Statistically significant differences were found in the volumetric and matching percentage data (P 0.05). InVesalius was the most accurate software program for 3D rendering of the dentition, with a volumetric bias (Mimics software) ranging from 4.59 to 85.79 mm3, while ITK-SNAP showed the highest volumetric bias, ranging from 30.22 to 319.83 mm3. The mismatched area was mainly located at the radicular tooth region. The volumetric data showed excellent inter-software reliability, with coefficient values ranging from 0.951 to 0.997. Different semi-automatic software algorithms could generate different patterns of inaccuracy error in the segmentation of teeth.

Electronic Method (Pro-Kin) for Improving And Speeding up The Recovery after Ankle Sprain.

Ankle sprains, very common injuries occurred especially during sports activities, are mainly caused by indirect trauma, which influences exaggerated stress exceeding the strength of stabilization mechanisms. Up to 85% of such injuries result from a sudden flexion and inversion of the foot. In this study, we analyzed the effectiveness of the platform Pro-kin, an innovative system that has given us the possibility to combine the functionality of the older proprioceptive boards with very accurate software in order to improve and accelerate the recovery after ankle injuries. 30 patients with moderate ankle sprain outcomes in two groups (A and B) were included in this study. Group A was only treated with proprioceptive exercises for 3 weeks, while the group B was trained with the innovative Pro-kin. In both groups, we evaluated VAS scale, the ratio between the number of circumductions performed by the injured foot and the time spent on doing them and the percentage of load among the injured and the healthy foot in statics and dynamics with electronic baropodometry. Our data has been collected at t0 (beginning of study), t1(one week later), t2 (two weeks later), t3 (one month later), t4 (two months later), and then analyzed by the two-way analysis of variance (2-way ANOVA) test. At t0 no statistical differences of pain in the 2 groups (3.3 and 3.4); the values were similar, as well as at time t1, t2, t3 and t4. Therefore we deduce that Pro-kin treatment is not painful. The number of circumductions performed was definitely better in B group since the first week; for the A group the values considerably increased only at t3 (one month later). Comparing the load percentages on two feet detected by the electronic baropodometer in statics and in dynamics, we deduced that the patients of A group tend to lean mostly on the healthy foot than B group. This study demonstrates that new technological resources (such as Pro-kin) may be helpful to improve and speed up the recovery of ankle sprain in athletes.

Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

ABSTRACTAccurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance.

A novel and fast electromagnetic and electrothermal software for quench analysis of high field magnets

Abstract High-field superconducting REBCO magnets contain several coils with many turns. For these magnets, electro-thermal quench is an issue that magnet designers need to take into account. Thus, there is a need for a fast and accurate software to numerically model the overall performance of full-scale magnets. High temperature superconductors can be modeled using different techniques for electro-magnetic and thermal (finite element method) analysis. However, it takes a lot of time to model the electro-magnetic and electro-thermal behavior of superconductors simultaneously, especially for non-insulated or metal-insulated coils. In addition, most of the available methods ignore screening currents, which are an important feature of REBCO magnets. We have developed a novel software programmed in C++, which performs coupled electro-magnetic and electro-thermal analysis using variational methods based on minimum electro-magnetic entropy production and finite difference, respectively. The developed software, which takes screening currents into account, is applied to axi-symmetric full scale magnets of more than 32 T field strength under the SuperEMFL project for thermal quench reliability during standard operation. We show that magnets incorporating non-insulated coils are more reliable against quench than metal insulated coils. Also, realistic cooling conditions at the boundaries are essential for such simulations. The model developed can be used for a quick and complete electro-magnetic and electro-thermal analysis of superconducting high field magnets.

International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation.

Total metabolic tumor volume (TMTV) is prognostic in lymphoma. However, cutoff values for risk stratification vary markedly, according to the tumor delineation method used. We aimed to create a standardized TMTV benchmark datasetallowing TMTV to be tested and applied as a reproducible biomarker. Methods: Sixty baseline 18F-FDG PET/CT scans were identified with a range of disease distributions (20 follicular, 20 Hodgkin, and 20 diffuse large B-cell lymphoma). TMTV was measured by 12 nuclear medicine experts, each analyzing 20 cases split across subtypes, with each case processed by 3-4 readers. LIFEx or ACCURATE software was chosen according to reader preference. Analysis was performed stepwise: TMTV1 with automated preselection of lesions using an SUV of at least 4 and a volume of at least 3 cm3 with single-click removal of physiologic uptake; TMTV2 with additional removal of reactive bone marrow and spleen with single clicks; TMTV3 with manual editing to remove other physiologic uptake, if required; and TMTV4 with optional addition of lesions using mouse clicks with an SUV of at least 4 (no volume threshold). Results: The final TMTV (TMTV4) ranged from 8 to 2,288 cm3, showing excellent agreement among all readers in 87% of cases (52/60) with a difference of less than 10% or less than 10 cm3 In 70% of the cases, TMTV4 equaled TMTV1, requiring no additional reader interaction. Differences in the TMTV4 were exclusively related to reader interpretation of lesion inclusion or physiologic high-uptake region removal, not to the choice of software. For 5 cases, large TMTV differences (>25%) were due to disagreement about inclusion of diffuse splenic uptake. Conclusion: The proposed segmentation method enabled highly reproducible TMTV measurements, with minimal reader interaction in 70% of the patients. The inclusion or exclusion of diffuse splenic uptake requires definition of specific criteria according to lymphoma subtype. The publicly available proposed benchmark allows comparison of study results and could serve as a reference to test improvements using other segmentation approaches.

BjEnet: a fast and accurate software bug localization method in natural language semantic space

bjEnet: a fast and accurate software bug localization method in natural language semantic space

Software cost and effort estimation using dragonfly whale optimized multilayer perceptron neural network

This paper proposed a Constructive Rapid Application Development Model based Software Cost Estimation Technique (CORADMO based SCET) for accurate software cost estimation. The data requirements, cost drivers, constraints and priorities are given as an input to the Fuzzy Inference System (FIS). The processed output such as effort, time and cost for nominal plan, shortest schedule plan, and least cost plan are computed in the Fuzzy Inference System (FIS). Further to reduce the effort time and cost means, the output optimized by Dragon fly Whale Optimization (DWO)which provides the best estimated effort, time and cost as an output for software development. The proposed CORADMO based SCET model is evaluated by NASA 93 dataset using MATLAB. The performance of the CORADMO based SCET approach is assessed in terms of Mean Magnitude of Relative Error, Pred (25%), and Magnitude of Relative Error attains the values of 98.77%, 92.55%, and 93.45% respectively. Finally, the CORADMO based SCET model justifies the suitability of Dragon fly Whale Optimization with the proposed fuzzy logic.

Rand-Index Target Projective Gradient Deep Belief Network for Software Fault Prediction

Predicting defective software modules before testing is a valuable operation that reduces time and cost of software testing. Source code fault prediction plays a vital role in improving software quality that effectively assists in optimization testing resource allocation. Several machine learning and ensemble learning techniques has been extensively evolved over the recent few years to predict defect at an early stage. These techniques made predictions based on historical defect data, the software metrics. Nevertheless, the time efficient and accurate fault predictions are the major challenging tasks that yet have to be addressed. In order to ensure accurate software fault prediction, a method called Rand-Index Target Projective Gradient Deep Belief Network (RTPGDBN) is designed. The proposed RTPGDBN method comprises of three processes namely acquiring JAVA packages, software metric selection and classification. First, the number of JAVA packages is used as input from the dataset. Second with the JAVA packages obtained as input, Rand similarity indexive target projection function is applied for selecting the most significant software metrics in order to minimize time complexity of fault prediction. Third, with the selected metrics, classification is performed using Tversky Gradient Deep Belief neural network. Also, gradient descent function is applied to minimize classification error, therefore ensuring accurate software fault classification results obtained at the output layer. Experimental setup of proposed RPGDDBN and existing methods are implemented in Java language and the dataset collected from smell prediction replication package. Performance analysis is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity and space complexity. Through extensive experiments on repository data, experimental results indicate that our RPGDDBN method outperforms two state-of-the-art defect detection methods in terms of different performance metrics.

Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning

Effective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase. This study aims to propose a comprehensive five-stage framework for software defect prediction, addressing the current challenges in the field. The first stage involves selecting a cleaned version of NASA’s defect datasets, including CM1, JM1, MC2, MW1, PC1, PC3, and PC4, ensuring the data’s integrity. In the second stage, a feature selection technique based on the genetic algorithm is applied to identify the optimal subset of features. In the third stage, three heterogeneous binary classifiers, namely random forest, support vector machine, and naïve Bayes, are implemented as base classifiers. Through iterative tuning, the classifiers are optimized to achieve the highest level of accuracy individually. In the fourth stage, an ensemble machine-learning technique known as voting is applied as a master classifier, leveraging the collective decision-making power of the base classifiers. The final stage evaluates the performance of the proposed framework using five widely recognized performance evaluation measures: precision, recall, accuracy, F-measure, and area under the curve. Experimental results demonstrate that the proposed framework outperforms state-of-the-art ensemble and base classifiers employed in software defect prediction and achieves a maximum accuracy of 95.1%, showing its effectiveness in accurately identifying software defects. The framework also evaluates its efficiency by calculating execution times. Notably, it exhibits enhanced efficiency, significantly reducing the execution times during the training and testing phases by an average of 51.52% and 52.31%, respectively. This reduction contributes to a more computationally economical solution for accurate software defect prediction.

Impelementasi Sak Emkm Dalam Penyusunan Laporan Keuangan Menggunakan Accurate Accounting Pada UMKM Hakuna Matata

The number of small, micro and medium enterprises (MSMEs) in Indonesia has increased significantly, but only a few MSMEs have implemented an accounting system in their financial records. Hakuna Matata MSMEs still carry out accounting records manually through recording expenses and receipts so that the owner does not know the exact amount of inventory and profit or loss earned. Manual recording can cause recording errors and the risk of losing data. The method used by the author in this research is descriptive qualitative with the type of case study. The data obtained is processed and then implemented into accurate software. From this research it can be concluded that the application of accurate software in Hakuna Matata MSMEs will ensure the preparation of financial reports more quickly and accurately, MSMEs can also find out the company's profit or loss. Computerized sales and purchase transactions also make it easy to find out inventory stock. The application of financial statements in accordance with SAK EMKM presents financial statements consisting of three components, namely the Statement of financial position, Income Statement and notes to financial statements prepared based on financial reports from accurate software.

Pendampingan Penyusunan Laporan Keuangan Oleh PT Arrasyid Trio Konsultindo Menggunakan Software Accurate Pada Pelaku UMKM

Micro Small and Medium Enterprises (MSMEs) are economic activities whose business scope is still in the small category and is usually only managed by individuals. PT Arrasyid Trio Konsultindo is the subject of this activity which acts as a financial and management consultant. The location of the implementation of mentoring activities is in 2 (two) places, namely the client PT Arrasyid Trio Konsultindo's office in Madiun, East Java and at the official office which is located at Perum Zada Regency 1, Block 11, RT 03 / RW 09, Bulakan, Sukoharjo, Central Java. CV. Argo Mulyo Sejahtera is the client who uses financial statement preparation assistance services. There are the problem encountered by CV. Argo Mulyo Sejahtera is the unavailability of financial reports that are systematic and aligns with financial accounting standard MSMEs (SAK EMKM). The purpose of the mentoring activities carried out by PT Arrasyid to CV. Argo Mulyo Sejahtera is to present more valid, effective and efficient financial statements. The methods used are observation and preparation, providing workshops on the importance of financial reports and Accurate Software, as well as monitoring and mentoring MSMEs. The results of the Financial Report Preparation Assistance are that MSME actors can find out how to prepare financial reports using Accurate software easily and efficiently.

OPTIMIZING THE COMPETENCE AND PROFESSIONALISM OF ACCOUNTING TEACHERS IN USING ACCURATE SOFTWARE

Based on the observations made, problems were found including accounting records that were still manual and a lack of understanding regarding accurate software. The lack of expertise in the use of technology in the accounting process creates a new problem, which is difficult to get a decent job for job seekers. Among job seekers, there are various educational backgrounds, ranging from high school graduates, vocational schools, as well as undergraduate graduates from various universities. The Muhammadiyah Sidoarjo University Community Service Center conducts training on Accurate accounting software for teachers and vocational students in Sidoarjo. Training is provided using Accurate software. The purpose of this training is to provide training to teachers and students in understanding and operating various features in Accurate related to transaction accounting which are expected to improve skills in using this software. The method in this service uses the PAR method which involves the teacher. The stages of implementing community service use the PAR method, namely initial mapping, implementation, and monitoring/evaluation. The results of the service carried out at the Muhammadiyah University of Sidoarjo went smoothly and the teachers succeeded in increasing teachers' understanding in using accurate applications.

Penerapan Sistem Informasi Akuntansi Menggunakan Software Accurate dalam Pengambilan Keputusan UMKM di Kota Mataram

Accurate is an accounting information system that is useful for facilitating the management of financial data for companies and MSMEs and producing information in the form of financial reports that can be used as a basis for decision making. So far there are still many MSMEs that have not implemented accounting information systems such as accurate software. This research is intended to find out how to implement an accounting information system using accurate software in decision making for MSMEs in Mataram City. The research method used was descriptive qualitative with data collection techniques through interviews and documentation with informants, namely each owner or manager and employees responsible for implementing Accurate Software from 4 MSMEs in Mataram City, including CV. Prabu Pande Perkasa, CV. Lancar Jaya Abadi, Geshuter Lombok, and Mahkota Lancar. The results of this research can be concluded that the application of Accurate Software to several MSMEs in Mataram City can be said to be quite effective according to TAM theory even though there is one drawback related to invoices which shows that there is an additional need for data processing. Then, in making decisions, MSMEs tend to take low risks, pay attention to basic information, and must have creative thinking.

PELATIHAN PENGGUNAAN ACCOUNTING SOFTWARE DALAM MENGELOLA INFORMASI KEUANGAN PADA SMK ERA UTAMA PANCUR BATU

The impact of technology in the world of education, especially in the field of accounting. Advances in Information Technology (IT) have an impact on the application and development of accounting science. The emergence of terms such as ebusiness and enterprise systems shows that the world of accounting will become increasingly complex, not just managing journals and preparing financial reports. The role of an accountant can cover three areas, namely designer, user and examiner. Therefore, vocational schools in the field of accounting need to have up-to-date knowledge regarding IT developments in accounting. Pantia PkM conducted a survey, the results found partner problems, namely, the need to improve the abilities and skills of Era Utama Pancur Batu Vocational School students in using IT and required knowledge sharing activities related to transaction management activities to producing financial reports using Accurate Software. Based on this problem, the solution given is to carry out community service activities (PkM) in the form of training in the form of workshops on using the Accurate 5 application. The aim of this PkM is to increase students' information technology knowledge and abilities in operating the Accurate Version 5 application. keyword: Accurate Software; accounting; Financial Information;

Feedforward neural network-based augmented salp swarm optimizer for accurate software development cost forecasting

This research proposes the use of feed-forward backpropagation neural networks (FFNN) to develop an accurate cost forecasting model in light of the challenges associated with forecasting software development costs (FSDC). The salp swarm algorithm (SSA) is first augmented and then employed to optimize the parameters of the developed FFNN predictor. A search enhancement mechanism and an elitism technique have been developed and incorporated into the SSA optimization process as two fresh and effective techniques for this goal. The search enhancement mechanism is employed to keep up a high rate of global exploration while also driving convergence towards the optimal area. Whereas elitism is used throughout the research phase to prevent stagnation in the local optima. Nineteen benchmark test functions and twelve benchmark software development cost forecasting data sets are utilized to assess the performance of the recommended enhancement techniques and developed algorithms. The results obtained from experiments show the superiority of the proposed techniques. In addition, the developed technique has been compared with many state-of-the-art methods, which demonstrates its durability. This is in addition to the statistical validation carried out on the results obtained, which also supports the robustness of the proposed technique.

Physical vapor deposition simulator by graphical processor unit ray casting

This paper presents fast, accurate software for modeling physical vapor deposition systems over irregular surfaces. The model is implemented using graphics processing unit (GPU) ray casting. Applied models are viewed as a cross section of the area of interest. Given evaporation rate, time, and angular profiles in a vacuum system, an iterative time-step approach for calculating deposition profiles is calculated in the GPU architecture following a ballistic modeling approach. Thin-film technologies for the electronics industry will require evaporations on complex surfaces. Depending on the nature of the surface, a uniform thin film across the topology is wanted for various device parameters. The ray casting method is tested against various profiles. The code is freely distributed on GitHub (see https://github.com/adam-r-thomas/PVDS).

Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments

Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.

Software Defect Prediction System Based on Decision Tree Algorithm

Software defect prediction plays a crucial role in ensuring software quality and minimizing the potential risks associated with defects. This study aims to develop a comprehensive software defect prediction system that utilizes tree-based algorithms to enhance accuracy, feature selection, and evaluation metrics. The study addresses the limitations of previous research by considering a broader range of datasets, comparing computational efficiency with other ensemble techniques, and examining the impact of hyperparameters on model performance. The implemented system consists of three stages: dataset loading, processing, and result presentation. The dataset loading page allows users to upload their datasets in CSV format, simplifying the prediction process. The processing page performs essential tasks such as feature engineering, normalization using minimax normalization, and training the model with the decision tree algorithm. These steps ensure the extraction of relevant features, transformation of data, and learning of patterns and correlations for accurate software defect prediction. The study emphasizes the practical implementation of the developed system, going beyond model evaluation. By providing a fully functional and integrated system, this study bridges the gap between research and real-world application. The findings of this study contribute to the field of software defect prediction by offering an improved system that enhances accuracy, feature selection, and evaluation metrics. This has implications for software development and quality assurance processes, ultimately leading to higher software quality and increased productivity.

Software Subclassification Based on BERTopic-BERT-BiLSTM Model

With the continuous influx of application software onto the application software market, achieving accurate software recommendations for users in the huge software application market is urgent. To address this issue, each application software market currently provides its own classification tags. However, several problems still exist, such as the lack of objectivity, hierarchy, and standardization in these classifications, which in turn affects the accuracy of precise software recommendations. Accordingly, a customized BERTopic model is proposed to cluster the software description texts of the application software and the automatic tagging and updating of the application software tags are realized according to the clusters obtained by topic clustering and the extracted subject words. At the same time, a data enhancement method based on the c-TF-IDF algorithm is proposed to solve the problem of imbalance of datasets, and then the classification model based on the BERT-BiLSTM model is trained on the labeled datasets to classify the software in the dimension of the application function, so as to realize the accurate software recommendation for users. Based on the experimental verification of two datasets, 21 categories in the SourceForge dataset and 19 categories in the Chinese App Store dataset are subclassed by the clustering results of the customized BERTopic model, and the tags of 138 subclasses and 262 subclasses are formed, respectively. In addition, a complete tagged software description text dataset is constructed and the software tags are updated automatically. In the first stage of the classification experiment, the weighted average accuracy, recall rate, and F1 value can reach 0.92, 0.91, and 0.92, respectively. In the second stage, the weighted average accuracy, recall rate, and F1 value can all reach 0.96. After data enhancement, the weighted average F1 value of the classification model can be increased by up to two percentage points.