Development and Application of Intelligent Prediction Software for Broken Rock Zone Thickness of Drifts

In order to seek the economical, practical and effective method of obtaining the thickness of broken rock zone, an emerging intelligent prediction method with adaptive neuro-fuzzy inference system (ANFIS) was introduced into the thickness prediction. And the software with functions of creating and applying prediction systems was devel- oped on the platform of MATLAB6.5. The software was used to predict the broken rock zone thickness of drifts at Li- angbei coal mine, Xinlong Company of Coal Industry in Xuchang city of Henan province. The results show that the predicted values accord well with the in situ measured ones. Thereby the validity of the software is validated and it provides a new approach to obtaining the broken zone thickness.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Software Reliability Prediction Using Ensemble Learning on Selected Features in Imbalanced and Balanced Datasets: A Review

Redundancy,correlation,feature irrelevance,and missing samples are just a few problems that make it difficult to analyze software defect data.Additionally,it might be challenging to maintain an even distribution of data relating to both defective and non-defective software.The latter software class’s data are predominately present in the dataset in the majority of experimental situations.The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification.Besides the successful feature selection approach,a novel variant of the ensemble learning technique is analyzed to address the challenges of feature redundancy and data imbalance,providing robustness in the classification process.To overcome these problems and lessen their impact on the fault classification performance,authors carefully integrate effective feature selection with ensemble learning models.Forward selection demonstrates that a significant area under the receiver operating curve(ROC)can be attributed to only a small subset of features.The Greedy forward selection(GFS)technique outperformed Pearson’s correlation method when evaluating feature selection techniques on the datasets.Ensemble learners,such as random forests(RF)and the proposed average probability ensemble(APE),demonstrate greater resistance to the impact of weak features when compared to weighted support vector machines(W-SVMs)and extreme learning machines(ELM).Furthermore,in the case of the NASA and Java datasets,the enhanced average probability ensemble model,which incorporates the Greedy forward selection technique with the average probability ensemble model,achieved remarkably high accuracy for the area under the ROC.It approached a value of 1.0,indicating exceptional performance.This review emphasizes the importance of meticulously selecting attributes in a software dataset to accurately classify damaged components.In addition,the suggested ensemble learning model successfully addressed the aforementioned problems with software data and produced outstanding classification performance.

Software Defect Prediction Based Ensemble Approach

Software systems have grown significantly and in complexity.As a result of these qualities,preventing software faults is extremely difficult.Software defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance costs.When it comes to lowering software costs and assuring software quality,SDP plays a critical role in software development.As a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more efficiently.Several methods for detecting and addressing such flaws at a low cost have been offered.These approaches,on the other hand,need to be significantly improved in terms of performance.Therefore in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are proposed.The proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative features.To find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository datasets.When compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.

Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

Moth Flame Optimization Based FCNN for Prediction of Bugs in Software

The software engineering technique makes it possible to create high-quality software.One of the most significant qualities of good software is that it is devoid of bugs.One of the most time-consuming and costly software proce-dures isfinding andfixing bugs.Although it is impossible to eradicate all bugs,it is feasible to reduce the number of bugs and their negative effects.To broaden the scope of bug prediction techniques and increase software quality,numerous causes of software problems must be identified,and successful bug prediction models must be implemented.This study employs a hybrid of Faster Convolution Neural Network and the Moth Flame Optimization(MFO)algorithm to forecast the number of bugs in software based on the program data itself,such as the line quantity in codes,methods characteristics,and other essential software aspects.Here,the MFO method is used to train the neural network to identify optimal weights.The proposed MFO-FCNN technique is compared with existing methods such as AdaBoost(AB),Random Forest(RF),K-Nearest Neighbour(KNN),K-Means Clustering(KMC),Support Vector Machine(SVM)and Bagging Clas-sifier(BC)are examples of machine learning(ML)techniques.The assessment method revealed that machine learning techniques may be employed successfully and through a high level of accuracy.The obtained data revealed that the proposed strategy outperforms the traditional approach.

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

Software defect prediction is a research hotspot in the field of software engineering.However,due to the limitations of current machine learning algorithms,we can’t achieve good effect for defect prediction by only using machine learning algorithms.In previous studies,some researchers used extreme learning machine(ELM)to conduct defect prediction.However,the initial weights and biases of the ELM are determined randomly,which reduces the prediction performance of ELM.Motivated by the idea of search based software engineering,we propose a novel software defect prediction model named KAEA based on kernel principal component analysis(KPCA),adaptive genetic algorithm,extreme learning machine and Adaboost algorithm,which has three main advantages:(1)KPCA can extract optimal representative features by leveraging a nonlinear mapping function;(2)We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM,so as to improve the generalization ability and prediction capacity of ELM;(3)We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor,which can further improve the effect of defect prediction.To effectively evaluate the performance of KAEA,we use eleven datasets from large open source projects,and compare the KAEA with four machine learning basic classifiers,ELM and its three variants.The experimental results show that KAEA is superior to these baseline models in most cases.

RFC:a feature selection algorithm for software defect prediction

Software defect prediction(SDP)is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects,so as to effectively predict defects in the new software.However,there are redundant and irrelevant features in the software defect datasets affecting the performance of defect predictors.In order to identify and remove the redundant and irrelevant features in software defect datasets,we propose ReliefF-based clustering(RFC),a clusterbased feature selection algorithm.Then,the correlation between features is calculated based on the symmetric uncertainty.According to the correlation degree,RFC partitions features into k clusters based on the k-medoids algorithm,and finally selects the representative features from each cluster to form the final feature subset.In the experiments,we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration(NASA)software defect prediction datasets in terms of area under curve(AUC)and Fvalue.The experimental results show that RFC can effectively improve the performance of SDP.

Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.

Software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

Developing successful software with no defects is one of the main goals of software projects.In order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital role.Machine learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated structure.In this paper,we aim to address such issues in predicting software defects.We propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects prone.We design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA datasets.The experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software defects.According to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software engineering.This,in turn,could lead to saving software development resources and producing more reliable software.

A Comparative Study of Three Machine Learning Methods for Software Fault Prediction

The contribution of this paper is comparing three popular machine learning methods for software fault prediction. They are classification tree, neural network and case-based reasoning. First, three different classifiers are built based on these three different approaches. Second, the three different classifiers utilize the same product metrics as predictor variables to identify the fault-prone components. Third, the predicting results are compared on two aspects, how good prediction capabilities these models are, and how the models support understanding a process represented by the data.

Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods

Complex systems exist widely,including medicines from natural products,functional foods,and biological samples.The biological activity of complex systems is often the result of the synergistic effect of multiple components.In the quality evaluation of complex samples,multicomponent quantitative analysis(MCQA)is usually needed.To overcome the difficulty in obtaining standard products,scholars have proposed achieving MCQA through the“single standard to determine multiple components(SSDMC)”approach.This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia.Depending on a convenient(ultra)high-performance liquid chromatography method,how can the repeatability and robustness of the MCQA method be improved?How can the chromatography conditions be optimized to improve the number of quantitative components?How can computer software technology be introduced to improve the efficiency of multicomponent analysis(MCA)?These are the key problems that remain to be solved in practical MCQA.First,this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years,as well as the method robustness and accuracy evaluation.Second,it also summarizes methods to improve peak capacity and quantitative accuracy in MCA,including column selection and twodimensional chromatographic analysis technology.Finally,computer software technologies for predicting chromatographic conditions and analytical parameters are introduced,which provides an idea for intelligent method development in MCA.This paper aims to provide methodological ideas for the improvement of complex system analysis,especially MCQA.

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features.In addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques.To address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic features.We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter.We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects.The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.

Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success.

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction.Most of the work present in software defect prediction is focused on the selection of best prediction techniques.For this purpose,deep learning and ensemble models have shown promising results.In contrast,there are very few researches that deals with cleaning the training data and selection of best parameter values from the data.Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy.To deal with this problem we used the Akaike information criterion(AIC)and the Bayesian information criterion(BIC)for selection of the best variables to train the model.A simple ANN model with one input,one output and two hidden layers was used for the training instead of a very deep and complex model.AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model.At first,variables were narrowed down to a smaller number using correlation values.Then subsets for all the possible variable combinations were formed.In the end,an artificial neural network(ANN)model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value.It was found that combination of only two variables’ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values.While,nm and npt is the worst combination and gives maximum AIC and BIC values.

A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction

Cross-project defect prediction （CPDP） uses the labeled data from external source software projects to com- pensate the shortage of useful data in the target project, in order to build a meaningful classification model. However, the distribution gap between software features extracted from the source and the target projects may be too large to make the mixed data useful for training. In this paper, we propose a cluster-based novel method FeSCH （Feature Selection Using Clusters of Hybrid-Data） to alleviate the distribution differences by feature selection. FeSCH includes two phases. Tile feature clustering phase clusters features using a density-based clustering method, and the feature selection phase selects features from each cluster using a ranking strategy. For CPDP, we design three different heuristic ranking strategies in the second phase. To investigate the prediction performance of FeSCH, we design experiments based on real-world software projects, and study the effects of design options in FeSCH （such as ranking strategy, feature selection ratio, and classifiers）. The experimental results prove the effectiveness of FeSCH. Firstly, compared with the state-of-the-art baseline methods, FeSCH achieves better performance and its performance is less affected by the classifiers used. Secondly, FeSCH enhances the performance by effectively selecting features across feature categories, and provides guidelines for selecting useful features for defect prediction.

A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning

Software Defect Prediction(SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years.However,the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets.Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction(CPDP).In this work,we propose a transfer-leaning algorithm(TSboostDF) that considers both knowledge transfer and class imbalance for CPDP.The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.

A feature selection approach based on a similarity measure for software defect prediction

Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class （defective or non-defective）, but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure （SM） for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor （KNN） model and measured by an area under curve （AUC） metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration （NASA） datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.