Journal articles on the topic 'Defect prediction model'
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1
Et.al, Christopher Paulraj. "An intelligent Model for Defect Prediction in Spot Welding." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 3991–4002. http://dx.doi.org/10.17762/turcomat.v12i3.1689.
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There are more than 30% defect in the spot welding of cars and randomly chosen cars are performed ultrasound or destructive testing. This makes the process very vulnerable and unpredictable. This results in huge reworks, productivity, monetary loss and negative impact on brand name.
This research paper presents the prediction of defect using machine learning models and as well forecasting models in spot welding through optimized methodology. This defect prediction model is useful in determining the defects that are likely to occur during spot welding. The forecasting model for process parameters data pattern, trends, etc. helps to identify the link between predicted defects. This model can evolve and improve over time by considering data from previous phases and history data of the spot welding cycle. Predicting the defects before testing begins improves the quality of the product being delivered and helps in planning and decision making for future spot welding.
The optimized defect prediction methodology in spot welding reduces the defects and predicted sample for testing which reduces the rework and increase the productivity, monetary value and brand name.
The experimental result shows that the spot-welding methodology has shown improvement over existing spot-welding method. Please see the six-sigma (Fig:13) chart for before and after improvement curve and value.
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Memon, Mashooque Ahmed, Mujeeb-ur-Rehman Maree Baloch, Muniba Memon, and Syed Hyder Abbas Musavi. "A Regression Analysis Based Model for Defect Learning and Prediction in Software Development." July 2021 40, no. 3 (July 1, 2021): 617–29. http://dx.doi.org/10.22581/muet1982.2103.15.
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The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.
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Yuan, Yuyu, Chenlong Li, and Jincui Yang. "An Improved Confounding Effect Model for Software Defect Prediction." Applied Sciences 13, no. 6 (March 8, 2023): 3459. http://dx.doi.org/10.3390/app13063459.
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Software defect prediction technology can effectively improve software quality. Depending on the code metrics, machine learning models are built to predict potential defects. Some researchers have indicated that the size metric could cause confounding effects and bias the prediction results. However, evidence shows that the real confounder should be the development cycle and number of developers, which could bring confounding effects when using code metrics for prediction. This paper proposes an improved confounding effect model, introducing a new confounding variable into the traditional model. On multiple projects, we experimentally analyzed the effect extent of the confounding variable. Furthermore, we verified that controlling confounding variables helps improve the predictive model’s performance.
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Zhang, Wei, Zhen Yu Ma, Qing Ling Lu, Xiao Bing Nie, and Juan Liu. "Research on Software Defect Prediction Method Based on Machine Learning." Applied Mechanics and Materials 687-691 (November 2014): 2182–85. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2182.
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This paper analyzed 44 metrics of application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defect, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density. On the basis of feature selection, we predicted defect density with 16 machine learning models for 33 actual software projects. The results show that the Spearman rank correlation coefficient (SRCC) between the predicting defect density and the actual defect density based on SVR model is 0.6727, higher than other 15 machine learning models, the model that has the second absolute value of SRCC is IBk model, the SRCC only is-0.3557, the results show that the method based on SVR has the highest prediction accuracy.
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Falessi, Davide, Aalok Ahluwalia, and Massimiliano DI Penta. "The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects." ACM Transactions on Software Engineering and Methodology 31, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3467895.
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Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.
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CHANG, CHING-PAO. "INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (March 2013): 147–72. http://dx.doi.org/10.1142/s0218194013500022.
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Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendations for action correction, using the Negative Association Rule mining technique. In addition to applying the association rule mining technique to build a High Defect Prediction Model (HDPM) to identify high defect action, the ACR builds a Low Defect Prediction Model (LDPM). For a submitted action, each HDPM rule used to predict the action as a high defect action and the LDPM rules are analyzed using negative association rule mining to spot the rule items with different characteristics in HDPM and LDPM rules. This information not only identifies the attributes required for corrections, but also provides a range (or a value) to facilitate the high defect action corrections. This study applies the ACR approach to a business software project to validate the efficiency of the proposed approach. The results show that the recommendations obtained can be applied to decrease software defect removal efforts.
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Nevendra, Meetesh, and Pradeep Singh. "Cross-Project Defect Prediction with Metrics Selection and Balancing Approach." Applied Computer Systems 27, no. 2 (December 1, 2022): 137–48. http://dx.doi.org/10.2478/acss-2022-0015.
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Abstract In software development, defects influence the quality and cost in an undesirable way. Software defect prediction (SDP) is one of the techniques which improves the software quality and testing efficiency by early identification of defects(bug/fault/error). Thus, several experiments have been suggested for defect prediction (DP) techniques. Mainly DP method utilises historical project data for constructing prediction models. SDP performs well within projects until there is an adequate amount of data accessible to train the models. However, if the data are inadequate or limited for the same project, the researchers mainly use Cross-Project Defect Prediction (CPDP). CPDP is a possible alternative option that refers to anticipating defects using prediction models built on historical data from other projects. CPDP is challenging due to its data distribution and domain difference problem. The proposed framework is an effective two-stage approach for CPDP, i.e., model generation and prediction process. In model generation phase, the conglomeration of different pre-processing, including feature selection and class reweights technique, is used to improve the initial data quality. Finally, a fine-tuned efficient bagging and boosting based hybrid ensemble model is developed, which avoids model over -fitting/under-fitting and helps enhance the prediction performance. In the prediction process phase, the generated model predicts the historical data from other projects, which has defects or clean. The framework is evaluated using25 software projects obtained from public repositories. The result analysis shows that the proposed model has achieved a 0.71±0.03 f1-score, which significantly improves the state-of-the-art approaches by 23 % to 60 %.
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Zhang, Jie, Gang Wang, Haobo Jiang, Fangzheng Zhao, and Guilin Tian. "Research and Appalication of Software Defect Predictionn based on BP-Migration learning." MATEC Web of Conferences 232 (2018): 03017. http://dx.doi.org/10.1051/matecconf/201823203017.
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Software Defect Prediction has been an important part of Software engineering research since the 1970s. This technique is used to calculate and analyze the measurement and defect information of the historical software module to complete the defect prediction of the new software module. Currently, most software defect prediction model is established on the basis of the same software project data set. The training date sets used to construct the model and the test data sets used to validate the model are from the same software projects. But in practice, for those has less historical data of a software project or new projects, the defect of traditional prediction method shows lower forecast performance. For the traditional method, when the historical data is insufficient, the software defect prediction model cannot be fully studied. It is difficult to achieve high prediction accuracy. In the process of cross-project prediction, the problem that we will faced is data distribution differences. For the above problems, this paper presents a software defect prediction model based on migration learning and traditional software defect prediction model. This model uses the existing project data sets to predict software defects across projects. The main work of this article includes: 1) Data preprocessing. This section includes data feature correlation analysis, noise reduction and so on, which effectively avoids the interference of over-fitting problem and noise data on prediction results. 2) Migrate learning. This section analyzes two different but related project data sets and reduces the impact of data distribution differences. 3) Artificial neural networks. According to class imbalance problems of the data set, using artificial neural network and dynamic selection training samples reduce the influence of prediction results because of the positive and negative samples data. The data set of the Relink project and AEEEM is studied to evaluate the performance of the f-measure and the ROC curve and AUC calculation. Experiments show that the model has high predictive performance.
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Peng, Xuemei. "Research on Software Defect Prediction and Analysis Based on Machine Learning." Journal of Physics: Conference Series 2173, no. 1 (January 1, 2022): 012043. http://dx.doi.org/10.1088/1742-6596/2173/1/012043.
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Abstract The defects of machine learning prediction technology can be more comprehensive and automatic learning model to find the defects in software has become the main method of defect prediction, selection and study of algorithm is the key to improve the accuracy and efficiency of machine learning. Comparing different machine learning defect prediction methods reveals that the algorithms have different advantages in different evaluation indicators, the use of these advantages and combining the stacking ensemble learning method in machine learning is put forward different prediction algorithm of prediction results. As software metrics and again the prediction model of software defect prediction combined machine learning algorithm is based on the experiment with the model of Eclipse, the data sets show the effectiveness of the model.
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Han, Wan Jiang, He Yang Jiang, Yi Sun, and Tian Bo Lu. "Software Defect Distribution Prediction for BOSS System." Applied Mechanics and Materials 701-702 (December 2014): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.67.
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Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects for BOSS project, which applies defect distribution model. Experiment results show that this approach can effectively improve the accuracy of defect prediction.
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CHANG, CHING-PAO, and CHIH-PING CHU. "SOFTWARE DEFECT PREDICTION USING INTERTRANSACTION ASSOCIATION RULE MINING." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (September 2009): 747–64. http://dx.doi.org/10.1142/s0218194009004428.
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Reducing the variance between expectation and execution of software processes is an essential activity for software development, in which the Causal Analysis is a conventional means of detecting problems in the software process. However, significant effort may be required to identify the problems of software development. Defect prevention prevents the problems from occurring, thus lowering the effort required in defect detection and correction. The prediction model is a conventional means of predicting the problems of subsequent process actions, where the prediction model can be built from the performed actions. This study proposes a novel approach that applies the Intertransaction Association Rule Mining techniques to the records of performed actions in order to discover the patterns that are likely to cause high severity defects. The discovered patterns can then be applied to predict the subsequent actions that may result in high severity defects.
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Liu, Can, Sumaya Sanober, Abu Sarwar Zamani, L. Rama Parvathy, Rahul Neware, and Abdul Wahab Rahmani. "Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network." Security and Communication Networks 2022 (April 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/5058461.
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Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.
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Liu, Can, Sumaya Sanober, Abu Sarwar Zamani, L. Rama Parvathy, Rahul Neware, and Abdul Wahab Rahmani. "Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network." Security and Communication Networks 2022 (April 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/5058461.
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Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.
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Shi, Sheng Li, Jin Shi, and Rui Wang. "A Prediction Model Based on ISOMAP for Software Defects." Applied Mechanics and Materials 347-350 (August 2013): 3278–82. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3278.
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To improve and guarantee the quality of software, it is very necessary to effectively predicting modules with defects in the software. There are usually more measure attributes in software quality prediction, which often leads to the curse of dimension. To do this, a new algorithm based on ISOMAP was presented to predict software defect, which combined manifold learning algorithms and classification methods. In the model, the high dimensional software metrics attribute data were firstly mapped into the low dimensional space through ISOMAP. Then the low dimensional features were classified with KNN, SVM and NB. Experiments demonstrate that the new model progresses the prediction precision of software defects as well as great improves the efficiency of the algorithm.
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Malhotra, Ruchika, and Shweta Meena. "Defect prediction model using transfer learning." Soft Computing 26, no. 10 (February 22, 2022): 4713–26. http://dx.doi.org/10.1007/s00500-022-06846-x.
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Jorayeva, Manzura, Akhan Akbulut, Cagatay Catal, and Alok Mishra. "Deep Learning-Based Defect Prediction for Mobile Applications." Sensors 22, no. 13 (June 23, 2022): 4734. http://dx.doi.org/10.3390/s22134734.
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Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.
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Han, Wan Jiang, Li Xin Jiang, Xiao Yan Zhang, and Yi Sun. "A Software Defect Prediction Model during the Test Period." Applied Mechanics and Materials 475-476 (December 2013): 1186–89. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1186.
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Effective defect prediction is an important topic in software engineering. This paper studies multiple defect prediction models and proposes a defect prediction model during the test period for organic project. This model is based on the analysis of project defect data and refer to Rayleigh model. Defect prediction model plays an important role in the analysis of software quality, rationally allocating resources of software test, improving the efficiency of software test. This paper selected representative software defect data to apply this model, which has been shown to improve project performance.
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Goel, Lipika, Neha Nandal, and Sonam Gupta. "An optimized approach for class imbalance problem in heterogeneous cross project defect prediction." F1000Research 11 (September 16, 2022): 1060. http://dx.doi.org/10.12688/f1000research.123616.1.
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Background: In recent studies, Cross Project Defect Prediction (CPDP) has proven to be feasible in software defect prediction. When both the source as well as the target projects have the same metric sets, it is termed as a homogeneous CPDP. Current CPDP strategies are difficult to implement through projects with a variety of different metric sets. Aside from that, training data often has a problem with class imbalance. The number of defective/bug-ridden and non-defective/clean instances of the source class is usually unbalanced. To address this issue, we propose a heterogeneous cross-project defect prediction framework that can predict defects across projects with different metric sets. Methods: To construct a prediction framework between projects with heterogeneous metric sets, our heterogeneous cross project defect prediction approach uses metric selection, metric matching, class imbalance (CIB) learning followed by ensemble modelling. For our study, we have considered six open-source object-oriented projects. Results: The proposed model resolved the class imbalance issue and records the highest recall value of 7.5 with f-score value as 7.4 in comparison with other baseline models. The highest AUC (area under curve) value of 0.86 has also been recorded. K fold cross validation was performed to evaluate the training accuracy of the model. The proposed optimized model was validated using the Wilcoxon signed rank test (WSR) with a significance level of 5% (i.e., P-value=0.05). Conclusions: Our empirical research on these six projects shows that predictions based on our methodology outperform or are statistically comparable to Within-Project Defect Prediction (WPDP) and other heterogeneous CPDP baseline models.
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Zhang, Shenggang, Shujuan Jiang, and Yue Yan. "A Software Defect Prediction Approach Based on BiGAN Anomaly Detection." Scientific Programming 2022 (April 13, 2022): 1–13. http://dx.doi.org/10.1155/2022/5024399.
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Software defect prediction usually is regarded as a classification problem, but classification models will face the class imbalance problem. Although there are many methods to solve the class imbalance problem, there is no method that can fundamentally solve the problem currently. In addition, supervised learning algorithms are always used to train defect prediction models, but obtaining a large amount of high-quality labelled data requires a lot of time and labor cost. In order to solve the class imbalance problem and eliminate the disadvantage of supervised learning, this paper attempts to predict software defects from a new perspective of anomaly detection. We propose an Anomaly Detection Model Based on BiGAN for Software Defect Prediction (ADGAN-SDP). The model proposed in this paper not only does not need to consider the class imbalance problem but also uses a semi-supervised method to train the model. Eight classification-based software defect prediction models are used as the baseline models and compared with ADGAN-SDP model. We evaluate ADGAN-SDP on 19 projects from NASA, AEEEM, and ReLink repositories. The experimental results show that the ADGAN-SDP model, which has a higher recall, outperforms all baseline models. It is suggested that the anomaly detection approach can be applied to the software defect prediction to fundamentally solve the class imbalance problem.
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Zhang, Hua Yin, and Jian Long Ding. "Weighted Hybrid Defect Content and Effectiveness Model." Advanced Materials Research 846-847 (November 2013): 1762–67. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1762.
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Requirements analysis phase is an important step which is at the earliest stage of a software lifecycle. If defects are found out at early stage, the cost of a project can be considerably minimized. A weighted method with higher accuracy is developed, based on a Hybrid Defect Content and Effectiveness Model (HDCE) used at requirements analysis phase. In this model, the defects of requirements are classified and every level has different weight value. Moreover, comparison of actual case data clearly indicates that prediction made from this model is more accurate than the prediction made from just using historical data model.
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Cui, Mengtian, Songlin Long, Yue Jiang, and Xu Na. "Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network." Entropy 24, no. 10 (September 27, 2022): 1373. http://dx.doi.org/10.3390/e24101373.
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The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a software defect prediction framework based on graph neural network from a complex network perspective. Firstly, we consider the software as a graph, where nodes represent the classes, and edges represent the dependencies between the classes. Then, we divide the graph into multiple subgraphs using the community detection algorithm. Thirdly, the representation vectors of the nodes are learned through the improved graph neural network model. Lastly, we use the representation vector of node to classify the software defects. The proposed model is tested on the PROMISE dataset, using two graph convolution methods, based on the spectral domain and spatial domain in the graph neural network. The investigation indicated that both convolution methods showed an improvement in various metrics, such as accuracy, F-measure, and MCC (Matthews correlation coefficient) by 86.6%, 85.8%, and 73.5%, and 87.5%, 85.9%, and 75.5%, respectively. The average improvement of various metrics was noted as 9.0%, 10.5%, and 17.5%, and 6.3%, 7.0%, and 12.1%, respectively, compared with the benchmark models.
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Pan, Cong, Minyan Lu, and Biao Xu. "An Empirical Study on Software Defect Prediction Using CodeBERT Model." Applied Sciences 11, no. 11 (May 23, 2021): 4793. http://dx.doi.org/10.3390/app11114793.
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Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.
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Vashisht, Rohit, and Syed Afzal Murtaza Rizvi. "An Empirical Study of Heterogeneous Cross-Project Defect Prediction Using Various Statistical Techniques." International Journal of e-Collaboration 17, no. 2 (April 2021): 55–71. http://dx.doi.org/10.4018/ijec.2021040104.
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Cross-project defect prediction (CPDP) forecasts flaws in a target project through defect prediction models (DPM) trained by defect data of another project. However, CPDP has a prevalent problem (i.e., distinct projects must have identical features to describe themselves). This article emphasizes on heterogeneous CPDP (HCPDP) modeling that does not require same metric set between two applications and builds DPM based on metrics showing comparable distribution in their values for a given pair of datasets. This paper evaluates empirically and theoretically HCPDP modeling, which comprises of three main phases: feature ranking and feature selection, metric matching, and finally, predicting defects in the target application. The research work has been experimented on 13 benchmarked datasets of three open source projects. Results show that performance of HCPDP is very much comparable to baseline within project defect prediction (WPDP) and XG boosting classification model gives best results when used in conjunction with Kendall's method of correlation as compared to other set of classifiers.
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Zhao, Yu, Yi Zhu, Qiao Yu, and Xiaoying Chen. "Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously." Symmetry 14, no. 2 (February 17, 2022): 401. http://dx.doi.org/10.3390/sym14020401.
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Software testing is the main method for finding software defects at present, and symmetric testing and other methods have been widely used, but these testing methods will cause a lot of waste of resources. Software defect prediction methods can reasonably allocate testing resources by predicting the defect tendency of software modules. Cross-project defect prediction methods have huge advantages when faced with missing datasets. However, most cross-project defect prediction methods are designed based on the settings of a single source project and a single target project. As the number of public datasets continues to grow, the number of source projects and defect information is increasing. Therefore, in the case of multi-source projects, this paper explores the problems existing when using multi-source projects for defect prediction. There are two problems. First, in practice, it is not possible to know in advance which source project is used to build the model to obtain the best prediction performance. Second, if an inappropriate source project is used in the experiment to build the model, it can lead to lower performance issues. According to the problems found in the experiment, the paper proposed a multi-source-based cross-project defect prediction method MSCPDP. Experimental results on the AEEEM dataset and PROMISE dataset show that the proposed MSCPDP method effectively solves the above two problems and outperforms most of the current state-of-art cross-project defect prediction methods on F1 and AUC. Compared with the six cross-project defect prediction methods, the F1 median is improved by 3.51%, 3.92%, 36.06%, 0.49%, 17.05%, and 9.49%, and the ACU median is improved by −3.42%, 8.78%, 0.96%, −2.21%, −7.94%, and 5.13%.
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Misirli, Ayse Tosun, Ayse Bener, and Resat Kale. "AI-Based Software Defect Predictors: Applications and Benefits in a Case Study." AI Magazine 32, no. 2 (June 5, 2011): 57. http://dx.doi.org/10.1609/aimag.v32i2.2348.
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Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent and hence, reduces post-release defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.
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Tosun, Ayse, Ayse Bener, and Resat Kale. "AI-Based Software Defect Predictors: Applications and Benefits in a Case Study." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 2 (July 11, 2010): 1748–55. http://dx.doi.org/10.1609/aaai.v24i2.18807.
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Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The application of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company during a period of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we have compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can be used as supportive tools during a new process implementation, predict 75% of code defects, and decrease the testing time compared with 25% of the code defects detected through more labor-intensive strategies such as code reviews and formal checklists.
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Almayyan, Waheeda. "Towards Predicting Software Defects with Clustering Techniques." International Journal of Artificial Intelligence & Applications 12, no. 1 (January 31, 2021): 39–54. http://dx.doi.org/10.5121/ijaia.2021.12103.
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The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer (GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number of features.
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Wang, Chongjiao, Changrong Yao, Bin Qiang, Siguang Zhao, and Yadong Li. "A Machine Learning Framework for Predicting Bridge Defect Detection Cost." Infrastructures 6, no. 11 (October 23, 2021): 152. http://dx.doi.org/10.3390/infrastructures6110152.
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Evaluating the cost of detecting bridge defects is a difficult task, but one that is vital to the lifecycle cost analysis of bridges. In this study, a detection cost sample database was established based on practical engineering data, and a bridge defect detection cost prediction model and software were developed using machine learning. First, the random forest method was adopted to evaluate the importance of the seven main factors affecting the detection cost. The most important indicators were selected, and the recent GDP growth rate was employed to account for the impact of social and economic developments on the detection cost. Combining a genetic algorithm with a multilayer neural network, a detection cost prediction model was established. The predictions given by this model were found to have an average relative error of 3.41%. Finally, an intelligent prediction software for bridge defect detection costs was established, providing a reliable reference for bridge lifecycle cost analysis and the evaluation of defect detection costs during the operation period.
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Zheng, Xianda, Yuan-Fang Li, Huan Gao, Yuncheng Hua, and Guilin Qi. "Towards Balanced Defect Prediction with Better Information Propagation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 759–67. http://dx.doi.org/10.1609/aaai.v35i1.16157.
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Defect prediction, the task of predicting the presence of defects in source code artifacts, has broad application in software development. Defect prediction faces two major challenges, label scarcity, where only a small percentage of code artifacts are labeled, and data imbalance, where the majority of labeled artifacts are non-defective. Moreover, current defect prediction methods ignore the impact of information propagation among code artifacts and this negligence leads to performance degradation. In this paper, we propose DPCAG, a novel model to address the above three issues. We treat code artifacts as nodes in a graph, and learn to propagate influence among neighboring nodes iteratively in an EM framework. DPCAG dynamically adjusts the contributions of each node and selects high-confidence nodes for data augmentation. Experimental results on real-world benchmark datasets show that DPCAG improves performance compare to the state-of-the-art models. In particular, DPCAG achieves substantial performance superiority when measured by Matthews Correlation Coefficient (MCC), a metric that is widely acknowledged to be the most suitable for imbalanced data.
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Grobler-Dębska, Katarzyna, Edyta Kucharska, and Jerzy Baranowski. "Formal scheduling method for zero-defect manufacturing." International Journal of Advanced Manufacturing Technology 118, no. 11-12 (October 22, 2021): 4139–59. http://dx.doi.org/10.1007/s00170-021-08104-0.
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AbstractA defect prevention is a part of manufacturing company practice. Paper proposes a formal approach for solving scheduling problems with unexpected events as extension of general frameworks for Zero-Defect Manufacturing (ZDM) strategy. ZDM aims to improve the process efficiency and the product quality while eliminating defects and minimizing process errors. However, most of ZDM applications focus on using the technological achievements of Industry 4.0 to detect and predict defects, forgetting to optimize the schedule on the production line. We propose formal method to create predictive-reactive schedule for problems with defect detection and repair. Our proposal is based on the formal Algebraic-Logical Meta-Model (ALMM). In particular, it uses the model switching method and combines defect detection, heuristics construction and decision support containing predictions of disturbances in the production process and enabling their prevention. Production defects are detected and repaired, and consequently, production delivers components without defects, and in the shortest possible time. Moreover, the collection and analysis of data related to the occurrence of disturbances in the production process helps the management board in making decisions based on analysis gathered and stored data. Thus, the proposed method includes strategies such as detection, repair, prediction and prevention for defect-free production. We illustrate the proposed method on the example of a flow-shop system with different types of product defect problem.
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Yi Zhu, Yi Zhu, Yu Zhao Yi Zhu, Qiao Yu Yu Zhao, and Xiaoying Chen Qiao Yu. "Cross-Project Defect Prediction Method based on Feature Distribution Alignment and Neighborhood Instance Selection." 網際網路技術學刊 23, no. 4 (July 2022): 761–69. http://dx.doi.org/10.53106/160792642022072304011.
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<p>In the practice of software project development, the developed project is a brand-new project. Defect prediction for this type of software project requires the use of other similar projects (i.e. source projects) to collect relevant data to build a defect prediction model, and make defect prediction for the project under development (i.e. target project). However, the prediction model built with the relevant data of the source project cannot achieve the ideal prediction performance when predicting the target project. The main reason is that there is a large data distribution difference between the source project and the target project. The data distribution difference is mainly in the distribution of features between projects and differences between instances. In response to the above problems, starting from both features and instances, a cross-project defect prediction method is proposed. This method first aligns the feature distribution based on the data of the existing target project and the source project data. Then, it selects the labeled instance that is similar to the unlabeled instance in the target project, and finally builds a defect prediction model based on the selected source project instances. Cross-project defect prediction experiments were carried out on the Relink datasets and the Promise datasets. Compared with the classic instance-based cross-project defect prediction method, significant improvements have been made in F-measure and AUC; compared with the prediction of within project defect prediction, it has achieved comparable performance.</p> <p> </p>
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Schmidt, Immo, Lorenz Dingeldein, David Hünemohr, Henrik Simon, and Max Weigert. "Application of Machine Learning Methods to Predict the Quality of Electric Circuit Boards of a Production Line." PHM Society European Conference 7, no. 1 (June 29, 2022): 550–55. http://dx.doi.org/10.36001/phme.2022.v7i1.3372.
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For the data challenge of the 2022 European PHM conference, data from a production line of electric circuit boards is provided to assess the quality of the produced components. The solution presented in this paper was elaborated to fulfill the data challenge objectives of predicting defects found in an automatic inspection at the end of the production line, predicting the result of a following human inspection and predicting the result of the repair of the defect components. Machine learning methods are used to accomplish the different prediction tasks. In order to build a reliable machine learning model, the steps of data preparation, feature engineering and model selection are performed. Finally, different models are chosen and implemented for the different sub-tasks. The prediction of defects in the automatic inspection is modeled with a multi-layer perceptron neural network, the prediction of human inspection is modeled using a random forest algorithm. For the prediction of human repair, a decision tree is implemented.
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Verna, Elisa, Gianfranco Genta, Maurizio Galetto, and Fiorenzo Franceschini. "Defects-per-unit control chart for assembled products based on defect prediction models." International Journal of Advanced Manufacturing Technology 119, no. 5-6 (October 31, 2021): 2835–46. http://dx.doi.org/10.1007/s00170-021-08157-1.
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AbstractTypically, monitoring quality characteristics of very personalized products is a difficult task due to the lack of experimental data. This is the typical case of processes where the production volume continues to shrink due to the growing complexity and customization of products, thus requiring low-volume productions. This paper presents a novel approach to statistically monitor defects-per-unit (DPU) of assembled products based on the use of defect prediction models. The innovative aspect of such DPU-chart is that, unlike conventional SPC charts requiring preliminary experimental data to estimate the control limits (phase I), it is constructed using a predictive model based on a priori knowledge of DPU. This defect prediction model is based on the structural complexity of the assembled product. By avoiding phase I, the novel approach may be of interest to researchers and practitioners to speed up the chart’s construction phase, especially in low-volume productions. The description of the method is supported by a real industrial case study in the electromechanical field.
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Wang, Yan. "Efficient Prediction Method of Defect of Monitor Configuration Software." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (March 20, 2019): 340–44. http://dx.doi.org/10.20965/jaciii.2019.p0340.
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In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of software defects. Experimental results show that the proposed method improves the quality of software effectively.
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Kim, Jun, and Ju Yeon Lee. "Development of a cost analysis-based defect-prediction system with a type error-weighted deep neural network algorithm." Journal of Computational Design and Engineering 9, no. 2 (February 25, 2022): 380–92. http://dx.doi.org/10.1093/jcde/qwac006.
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Abstract With the growing interest in smart factories, defect-prediction algorithms using data analysis techniques are being developed and applied to solve problems caused by defects at manufacturing sites. Cost benefit is an important factor to consider, and can be obtained by applying such algorithms. Existing defect-prediction algorithms usually aim to reduce the error rate of the prediction model, rather than focusing on the cost benefit for the practical application of defect-prediction models. Therefore, this study develops a defect-prediction algorithm considering costs and systematization for field application. To this end, a type error-weighted deep neural network (TEW-DNN) is proposed that applies a loss function to set a different weight for each type error, and cost analysis is conducted to search the optimal type error weight. A cost analysis-based defect-prediction system is designed considering the TEW-DNN algorithm and a cyber-physical system environment. The efficacy of the designed system is demonstrated through a case study involving the application of the system in a die-casting factory in South Korea.
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Malhotra, Ruchika, and Juhi Jain. "Predicting defects in imbalanced data using resampling methods: an empirical investigation." PeerJ Computer Science 8 (April 29, 2022): e573. http://dx.doi.org/10.7717/peerj-cs.573.
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The development of correct and effective software defect prediction (SDP) models is one of the utmost needs of the software industry. Statistics of many defect-related open-source data sets depict the class imbalance problem in object-oriented projects. Models trained on imbalanced data leads to inaccurate future predictions owing to biased learning and ineffective defect prediction. In addition to this large number of software metrics degrades the model performance. This study aims at (1) identification of useful metrics in the software using correlation feature selection, (2) extensive comparative analysis of 10 resampling methods to generate effective machine learning models for imbalanced data, (3) inclusion of stable performance evaluators—AUC, GMean, and Balance and (4) integration of statistical validation of results. The impact of 10 resampling methods is analyzed on selected features of 12 object-oriented Apache datasets using 15 machine learning techniques. The performances of developed models are analyzed using AUC, GMean, Balance, and sensitivity. Statistical results advocate the use of resampling methods to improve SDP. Random oversampling portrays the best predictive capability of developed defect prediction models. The study provides a guideline for identifying metrics that are influential for SDP. The performances of oversampling methods are superior to undersampling methods.
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Holovský, Jakub, Michael Stuckelberger, Tomáš Finsterle, Brianna Conrad, Amalraj Peter Amalathas, Martin Müller, and Franz-Josef Haug. "Towards Quantitative Interpretation of Fourier-Transform Photocurrent Spectroscopy on Thin-Film Solar Cells." Coatings 10, no. 9 (August 25, 2020): 820. http://dx.doi.org/10.3390/coatings10090820.
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The method of detecting deep defects in photovoltaic materials by Fourier-Transform Photocurrent Spectroscopy has gone through continuous development during the last two decades. Still, giving quantitative predictions of photovoltaic device performance is a challenging task. As new materials appear, a prediction of potentially achievable open-circuit voltage with respect to bandgap is highly desirable. From thermodynamics, a prediction can be made based on the radiative limit, neglecting non-radiative recombination and carrier transport effects. Beyond this, more accurate analysis has to be done. First, the absolute defect density has to be calculated, taking into account optical effects, such as absorption enhancement, due to scattering. Secondly, the electrical effect of thickness variation has to be addressed. We analyzed a series of state-of-the-art hydrogenated amorphous silicon solar cells of different thicknesses at different states of light soaking degradation. Based on a combination of empirical results with optical, electrical and thermodynamic simulations, we provide a predictive model of the open-circuit voltage of a device with a given defect density and absorber thickness. We observed that, rather than the defect density or thickness alone, it is their product or the total number of defects, that matters. Alternatively, including defect absorption into the thermodynamic radiative limit gives close upper bounds to the open-circuit voltage with the advantage of a much easier evaluation.
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Tallian, T. E. "Simplified Contact Fatigue Life Prediction Model—Part II: New Model." Journal of Tribology 114, no. 2 (April 1, 1992): 214–20. http://dx.doi.org/10.1115/1.2920876.
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Part I of this two-part paper reviews eleven published models as a basis for the construction of a simplified, readily calculated engineering model encompassing the major life variables, based on explicit assumptions and derivations. Part II of the paper presents the new model. It uses the applied alternating shear stress field to define the critical stress as a function of depth, and computes life as crack propagation time through this field. The model defines surface defects as critical defects. Material fatigue susceptibility, fatigue limit stress and the defect severity distribution are the main endurance parameters. In addition to Hertz pressure, life-modifying variables are: interface traction, surface microgeometry and EHD film. The influence of the variables in the new model on life distribution and on load/life law are compared to the reviewed models and it is shown that the new model is capable of representing the influence of all major consensus parameters on life, by relationships that fall within the bounds of previously published behavior.
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Olaleye, T. O., O. T. Arogundade, Sanjay Misra, A. Abayomi-Alli, and Utku Kose. "Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions." Scientific Programming 2023 (February 2, 2023): 1–18. http://dx.doi.org/10.1155/2023/6221388.
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Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry.
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Vijaya Kumar, Suria Devi, Saravanan Karuppanan, and Mark Ovinis. "Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings." Materials 15, no. 6 (March 18, 2022): 2259. http://dx.doi.org/10.3390/ma15062259.
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Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis.
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Su, Qiang, Lei Liu, and Shengjie Lai. "Measuring the assembly quality from the operator mistake view: a case study." Assembly Automation 29, no. 4 (September 25, 2009): 332–40. http://dx.doi.org/10.1108/01445150910987745.
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PurposeThe purpose of this paper is to focus on the assembly quality of copier products, specifically, concentrating on the prediction of the operator‐induced assembly defect.Design/methodology/approachBased on the Shibata model, the design‐based assembly complexity is redesigned. And the Sony Standard Time is replaced by the Fuji Xerox Standard Time in the calculation of the process‐based assembly complexity. Furthermore, different correlation functions are attempted and comparatively studied in the regression analysis. Thereby, a new defect rate prediction model is proposed and validated with three copier assembly cases.FindingsThe new proposed model is much more accurate and stable in the human‐induced assembly defect prediction in copier production.Practical implicationsThe proposed model can be used to ensure the assembly quality by removing potential defects at the structure and process design stages. Meanwhile, with this model, the interactions between the engineers and designers can be more effective.Originality/valueThis paper presents a novel assembly defect rate prediction model for copier assembly quality management.
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Kakkar, Misha, Sarika Jain, Abhay Bansal, and P. S. Grover. "Nonlinear Geometric Framework for Software Defect Prediction." International Journal of Decision Support System Technology 12, no. 3 (July 2020): 85–100. http://dx.doi.org/10.4018/ijdsst.2020070105.
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Humans use the software in every walk of life thus it is essential to have the best quality software. Software defect prediction models assist in identifying defect prone modules with the help of historical data, which in turn improves software quality. Historical data consists of data related to modules /files/classes which are labeled as buggy or clean. As the number of buggy artifacts as less as compared to clean artifacts, the nature of historical data becomes imbalance. Due to this uneven distribution of the data, it difficult for classification algorithms to build highly effective SDP models. The objective of this study is to propose a new nonlinear geometric framework based on SMOTE and ensemble learning to improve the performance of SDP models. The study combines the traditional SMOTE algorithm and the novel ensemble Support Vector Machine (SVM) is used to develop the proposed framework called SMEnsemble. SMOTE algorithm handles the class imbalance problem by generating synthetic instances of the minority class. Ensemble learning generates multiple classification models to select the best performing SDP model. For experimentation, datasets from three different software repositories that contain both open source as well as proprietary projects are used in the study. The results show that SMEnsemble performs better than traditional methods for identifying the minority class i.e. buggy artifacts. Also, the proposed model performance is better than the latest state of Art SDP model- SMOTUNED. The proposed model is capable of handling imbalance classes when compared with traditional methods. Also, by carefully selecting the number of ensembles high performance can be achieved in less time.
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Vashisht, Rohit, and Syed Afzal Murtaza Rizvi. "Estimation of Target Defect Prediction Coverage in Heterogeneous Cross Software Projects." International Journal of Information System Modeling and Design 12, no. 1 (January 2021): 73–93. http://dx.doi.org/10.4018/ijismd.2021010104.
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Heterogeneous cross-project defect prediction (HCPDP) is an evolving area under quality assurance domain which aims to predict defects in a target project that has restricted historical defect data as well as completely non-uniform software metrics from other projects using a model built on another source project. The article discusses a particular source project group's problem of defect prediction coverage (DPC) and also proposes a novel two phase model for addressing this issue in HCPDP. The study has evaluated DPC on 13 benchmarked datasets in three open source software projects. One hundred percent of DPC is achieved with higher defect prediction accuracy for two project group pairs. The issue of partial DPC is found in third prediction pairs and a new strategy is proposed in the research study to overcome this issue. Furthermore, this paper compares HCPDP modeling with reference to with-in project defect prediction (WPDP), both empirically and theoretically, and it is found that the performance of WPDP is highly comparable to HCPDP and gradient boosting method performs best among all three classifiers.
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李勇, 李勇, Ming Wen Yong Li, Zhandong Liu Ming Wen, and Haijun Zhang Zhandong Liu. "Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction." 網際網路技術學刊 23, no. 4 (July 2022): 779–89. http://dx.doi.org/10.53106/160792642022072304013.
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<p>Cross-project defect prediction (CPDP) is a field of study that allows predicting defects in software projects for which the availability of data is limited and produces generalizable prediction models. Due to the heterogeneity of cross projects, CPDP is particularly challenging and several methods have been employed to address this problem. Nevertheless, the class-imbalanced characteristic of the cross-project defect data also increases the learning difficulty of such a task but has not been investigated in depth. This paper proposed a novel, cost-cognitive ensemble method for CPDP, which includes four phases: bagging balanced resampling phase, base classifiers learning phase, cost value cognitive phase, and base classifiers ensemble phase. These phases create a composition of classifiers that are used for predicting defects. Results of an empirical evaluation on 10 datasets from the PROMISE repository indicated that our method achieves the best overall performance with respect to conventional methods. Moreover, our method could cognize the cost value automatically during the model training, it is shown to be more effective and practical.</p> <p> </p>
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Sinaga, Benyamin Langgu, Sabrina Ahmad, Zuraida Abal Abas, and Intan Ermahani A. Jalil. "A recommendation system of training data selection method for cross-project defect prediction." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (August 1, 2022): 990. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp990-1006.
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Cross-project <span lang="EN-US">defect prediction (CPDP) has been a popular approach to address the limited historical dataset when building a defect prediction model. Directly applying cross-project datasets to learn the prediction model produces an unsatisfactory predictive model. Therefore, the selection of training data is essential. Many studies have examined the effectiveness of training data selection methods, and the best-performing method varied across datasets. While no method consistently outperformed the others across all datasets, predicting the best method for a specific dataset is essential. This study proposed a recommendation system to select the most suitable training data selection method in the CPDP setting. We evaluated the proposed system using 44 datasets, 13 training data selection methods, and six classification algorithms. The findings concluded that the recommendation system effectively recommends the best method to select training data.</span>
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Chen, Chao, and Xingyuan Zhang. "Research on laser ultrasonic surface defect identification based on a support vector machine." Science Progress 104, no. 4 (October 2021): 003685042110590. http://dx.doi.org/10.1177/00368504211059038.
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To solve the problem of difficult quantitative identification of surface defect depth during laser ultrasonic inspection, a support vector machine-based method for quantitative identification of surface rectangular defect depth is proposed. Based on the thermal-elastic mechanism, the finite element model for laser ultrasound inspection of aluminum materials containing surface defects was developed by using the finite element software COMSOL. The interaction process between laser ultrasound and rectangular defects was simulated, and the reflected wave signals corresponding to defects of different depths under pulsed laser irradiation were obtained. Laser ultrasonic detection experiments were conducted for surface defects of different depths, and multiple sets of ultrasonic signal waveform were collected, and several feature vectors such as time-domain peak, center frequency peak, waveform factor and peak factor were extracted by using MATLAB, the quantitative defect depth identification model based on support vector machine was established. The experimental results show that the laser ultrasonic surface defect identification model based on support vector machine can achieve high accuracy prediction of defect depth, the regression coefficient of determination is kept above 0.95, and the average relative error between the true value and the predicted value is kept below 10%, and the prediction accuracy is better than that of the reflection echo method and BP neural network model.
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Liu, Wenjian, Baoping Wang, and Wennan Wang. "Deep Learning Software Defect Prediction Methods for Cloud Environments Research." Scientific Programming 2021 (November 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/2323100.
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This paper provides an in-depth study and analysis of software defect prediction methods in a cloud environment and uses a deep learning approach to justify software prediction. A cost penalty term is added to the supervised part of the deep ladder network; that is, the misclassification cost of different classes is added to the model. A cost-sensitive deep ladder network-based software defect prediction model is proposed, which effectively mitigates the negative impact of the class imbalance problem on defect prediction. To address the problem of lack or insufficiency of historical data from the same project, a flow learning-based geodesic cross-project software defect prediction method is proposed. Drawing on data information from other projects, a migration learning approach was used to embed the source and target datasets into a Gaussian manifold. The kernel encapsulates the incremental changes between the differences and commonalities between the two domains. To this point, the subspace is the space of two distributional approximations formed by the source and target data transformations, with traditional in-project software defect classifiers used to predict labels. It is found that real-time defect prediction is more practical because it has a smaller amount of code to review; only individual changes need to be reviewed rather than entire files or packages while making it easier for developers to assign fixes to defects. More importantly, this paper combines deep belief network techniques with real-time defect prediction at a fine-grained level and TCA techniques to deal with data imbalance and proposes an improved deep belief network approach for real-time defect prediction, while trying to change the machine learning classifier underlying DBN for different experimental studies, and the results not only validate the effectiveness of using TCA techniques to solve the data imbalance problem but also show that the defect prediction model learned by the improved method in this paper has better prediction performance.
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Lamba, Tripti, Kavita, and A. K. Mishra. "Optimal Machine learning Model for Software Defect Prediction." International Journal of Intelligent Systems and Applications 11, no. 2 (February 8, 2019): 36–48. http://dx.doi.org/10.5815/ijisa.2019.02.05.
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., Shaik Nafeez Umar. "SOFTWARE TESTING DEFECT PREDICTION MODEL - A PRACTICAL APPROACH." International Journal of Research in Engineering and Technology 02, no. 05 (May 25, 2013): 741–45. http://dx.doi.org/10.15623/ijret.2013.0205001.
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Shibo, Wang, Li Yong, Mi Wenbo, and Liu Ying. "Software Defect Prediction Incremental Model using Ensemble Learning." International Journal of Performability Engineering 16, no. 11 (2020): 1771. http://dx.doi.org/10.23940/ijpe.20.11.p9.17711780.
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