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Zhang, Wei, Zhen Yu Ma, Wen Ge Zhang, Qing Ling Lu, and Xiao Bing Nie. "Correlation Analysis of Software Defects Density and Metrics." Applied Mechanics and Materials 713-715 (January 2015): 2225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2225.
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It is very useful for improving software quality if we can find which software metrics are more correlative with software defects or defects density. Based on 33 actual software projects, we analyzed 44 software metrics from 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 defects, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density, these metrics can be used for improving software quality and predicting software defects density.
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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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Verma, Dinesh, and Shishir Kumar. "An Improved Approach for Reduction of Defect Density Using Optimal Module Sizes." Advances in Software Engineering 2014 (August 24, 2014): 1–7. http://dx.doi.org/10.1155/2014/803530.
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Nowadays, software developers are facing challenges in minimizing the number of defects during the software development. Using defect density parameter, developers can identify the possibilities of improvements in the product. Since the total number of defects depends on module size, so there is need to calculate the optimal size of the module to minimize the defect density. In this paper, an improved model has been formulated that indicates the relationship between defect density and variable size of modules. This relationship could be used for optimization of overall defect density using an effective distribution of modules sizes. Three available data sets related to concern aspect have been examined with the proposed model by taking the distinct values of variables and parameter by putting some constraint on parameters. Curve fitting method has been used to obtain the size of module with minimum defect density. Goodness of fit measures has been performed to validate the proposed model for data sets. The defect density can be optimized by effective distribution of size of modules. The larger modules can be broken into smaller modules and smaller modules can be merged to minimize the overall defect density.
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SCHNEIDEWIND, NORMAN. "COMPLEXITY-DRIVEN RELIABILITY MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 05 (2008): 479–94. http://dx.doi.org/10.1142/s0218539308003179.
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A model of software complexity and reliability is developed that uses an evolutionary process to transition from one software system to the next while complexity metrics are used to predict the reliability for each system. Systems are tested until the software passes defect presence criteria and is released. Testing criteria are based on defect count, defect density, and testing efficiency predictions exceeding specified thresholds. In addition, another type of testing efficiency — a directed graph representing the complexity of the software and defects embedded in the code — is used to evaluate the efficiency of defect detection in NASA satellite system software. Complexity metrics were found to be good predictors of defects and testing efficiency in this evolutionary process.
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Sherriff, Mark. "Utilizing verification and validation certificates to estimate software defect density." ACM SIGSOFT Software Engineering Notes 30, no. 5 (2005): 381–84. http://dx.doi.org/10.1145/1095430.1081768.
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Pipitone, J., and S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models." Geoscientific Model Development 5, no. 4 (2012): 1009–22. http://dx.doi.org/10.5194/gmd-5-1009-2012.
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Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model, one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.
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Pipitone, J., and S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models." Geoscientific Model Development Discussions 5, no. 1 (2012): 347–82. http://dx.doi.org/10.5194/gmdd-5-347-2012.
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Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.
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Nugroho, Ariadi, and Michel R. V. Chaudron. "The impact of UML modeling on defect density and defect resolution time in a proprietary system." Empirical Software Engineering 19, no. 4 (2013): 926–54. http://dx.doi.org/10.1007/s10664-013-9243-2.
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Concas, Giulio, Michele Marchesi, Cristina Monni, Matteo Orrù, and Roberto Tonelli. "Software Quality and Community Structure in Java Software Networks." International Journal of Software Engineering and Knowledge Engineering 27, no. 07 (2017): 1063–96. http://dx.doi.org/10.1142/s0218194017500401.
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We present a study of 600 Java software networks with the aim of characterizing the relationship among their defectiveness and community metrics. We analyze the community structure of such networks, defined as their topological division into subnetworks of densely connected nodes. A high density of connections represents a higher level of cooperation between classes, so a well-defined division in communities could indicate that the software system has been designed in a modular fashion and all its functionalities are well separated. We show how the community structure can be an indicator of well-written, high quality code by retrieving the communities of the analyzed systems and by ranking their division in communities through the built-in metric called modularity. We found that the software systems with highest modularity possess the majority of bugs, and tested whether this result is related to some confounding effect. We found two power laws relating the maximum defect density with two different metrics: the number of detected communities inside a software network and the clustering coefficient. We finally found a linear correlation between clustering coefficient and number of communities. Our results can be used to make predictive hypotheses about software defectiveness of future releases of the analyzed systems.
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Amara, Dalila, Ezzeddine Fatnassi, and Latifa Ben Arfa Rabai. "An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator." Scientific Programming 2021 (February 19, 2021): 1–20. http://dx.doi.org/10.1155/2021/8325417.
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Software metrics which are language-dependent are proposed as quantitative measures to assess internal quality factors for both method and class levels like cohesion and complexity. The external quality factors like reliability and maintainability are in general predicted using different metrics of internal attributes. Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al. (2014) to assess program redundancy using Shannon entropy measure. The main objective of these metrics is to monitor program reliability. Despite their important purpose, they are manually computed and only theoretically validated. Therefore, this paper aims to assess the redundancy metrics and empirically validate them as significant reliability indicators. As software reliability is an external attribute that cannot be directly evaluated, we employ other measurable quality factors that represent direct reflections of this attribute. Among these factors, defect density is widely used to measure and predict software reliability based on software metrics. Therefore, a linear regression technique is used to show the usefulness of these metrics as significant indicators of software defect density. A quantitative model is then proposed to predict software defect density based on redundancy metrics in order to monitor software reliability.
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Palak, Palak, Preeti Gulia, and Nasib Singh Gill. "Hybrid swarm intelligence-based software testing techniques for improving quality of component based software." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1716. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1716-1722.
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Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage.
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Shangfei Xie, and Zhili Sun. "Improve Navigation Software Quality by Using Prediction Model of Coding Stage Defect Density." Journal of Convergence Information Technology 6, no. 4 (2011): 64–69. http://dx.doi.org/10.4156/jcit.vol6.issue4.8.
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Sreenivasan, Srijith, and Manimaran Sundaram. "Process performance model for predicting Delivered Defect Density in a software scrum project." IOSR Journal of Computer Engineering 18, no. 05 (2016): 60–73. http://dx.doi.org/10.9790/0661-1805046073.
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John, Boby, and Rajeshwar S. Kadadevaramath. "Optimization of software development life cycle process to minimize the delivered defect density." OPSEARCH 56, no. 4 (2019): 1199–212. http://dx.doi.org/10.1007/s12597-019-00414-y.
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Catal, Cagatay. "A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction." Journal of Intelligent Systems 23, no. 1 (2014): 75–82. http://dx.doi.org/10.1515/jisys-2013-0030.
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AbstractPredicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation (LDS), support vector machine (SVM), expectation-maximization (EM-SEMI), and class mass normalization (CMN) methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data.
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Zheng, Shang, Jinjing Gai, Hualong Yu, Haitao Zou, and Shang Gao. "Software Defect Prediction Based on Fuzzy Weighted Extreme Learning Machine with Relative Density Information." Scientific Programming 2020 (November 18, 2020): 1–18. http://dx.doi.org/10.1155/2020/8852705.
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To identify software modules that are more likely to be defective, machine learning has been used to construct software defect prediction (SDP) models. However, several previous works have found that the imbalanced nature of software defective data can decrease the model performance. In this paper, we discussed the issue of how to improve imbalanced data distribution in the context of SDP, which can benefit software defect prediction with the aim of finding better methods. Firstly, a relative density was introduced to reflect the significance of each instance within its class, which is irrelevant to the scale of data distribution in feature space; hence, it can be more robust than the absolute distance information. Secondly, a K-nearest-neighbors-based probability density estimation (KNN-PDE) alike strategy was utilised to calculate the relative density of each training instance. Furthermore, the fuzzy memberships of sample were designed based on relative density in order to eliminate classification error coming from noise and outlier samples. Finally, two algorithms were proposed to train software defect prediction models based on the weighted extreme learning machine. This paper compared the proposed algorithms with traditional SDP methods on the benchmark data sets. It was proved that the proposed methods have much better overall performance in terms of the measures including G-mean, AUC, and Balance. The proposed algorithms are more robust and adaptive for SDP data distribution types and can more accurately estimate the significance of each instance and assign the identical total fuzzy coefficients for two different classes without considering the impact of data scale.
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Troupis, John M., Alex Karge, Sujith Seneviratne, et al. "Myocardial density analysis utilizing automated myocardial defect analysis software on resting 320-detector MDCT." International Journal of Cardiovascular Imaging 29, no. 5 (2013): 1121–27. http://dx.doi.org/10.1007/s10554-012-0171-9.
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Nelson, J. E., T. Zanon, J. G. Brown, et al. "Extracting Defect Density and Size Distributions from Product ICs." IEEE Design & Test of Computers 23, no. 5 (2006): 390–400. http://dx.doi.org/10.1109/mdt.2006.117.
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Liu, Zhiping, Xingle Liu, Lei Jiang, Ge Lu, and Huilong Liu. "Study on the Heat Transfer Characteristics Performed in the Infrared Thermography Detection of Welded Structure." Open Mechanical Engineering Journal 9, no. 1 (2015): 251–59. http://dx.doi.org/10.2174/1874155x01509010251.
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During the weld defect detection, the heat transfer characteristics are closely related to the material properties of welded structure. Based on the electromagnetic induction infrared thermography technology, the heat transfer Characteristics of the welded material are studied with the changing temperature. By using the finite element analysis software COMSOL, the eddy current density and temperature distributions and the law of thermal diffusion were analyzed which provide a reference for the study of heat transfer characteristics of weld defects. The internal temperature dynamic changes of the weld with surface crack or near-surface crack were also discussed. The appropriate time to observe and the key defect location on the steel obtained from the heat conduction process can be applied to the development of heat transfer characteristic analysis for steel weld and weld defects detection.
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Magalhães Pereira, Monica, and Luigi Carro. "Dynamic Reconfigurable Computing: The Alternative to Homogeneous Multicores under Massive Defect Rates." International Journal of Reconfigurable Computing 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/452589.
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The aggressive scaling of CMOS technology has increased the density and allowed the integration of multiple processors into a single chip. Although solutions based on MPSoC architectures can increase application's speed through TLP exploitation, this speedup is still limited to the amount of parallelism available in the application, as demonstrated by Amdahl's Law. Moreover, with the continuous shrinking of device features, very aggressive defect rates are expected for new technologies. Under high defect rates a large amount of processors of the MPSoC will be susceptible to defects and consequently will fail, not only reducing yield but also severely affecting the expected performance. This paper presents a run-time adaptive architecture that allows software execution even under aggressive defect rates. The proposed architecture can accelerate not only highly parallel applications but also sequential ones, and it is a heterogeneous solution to overcome the performance penalty that is imposed to homogeneous MPSoCs under massive defect rates.
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ELBERZHAGER, FRANK, STEPHAN KREMER, JÜRGEN MÜNCH, and DANILO ASSMANN. "FOCUSING TESTING BY USING INSPECTION AND PRODUCT METRICS." International Journal of Software Engineering and Knowledge Engineering 23, no. 04 (2013): 433–62. http://dx.doi.org/10.1142/s0218194013400093.
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A well-known approach for identifying defect-prone parts of software in order to focus testing is to use different kinds of product metrics such as size or complexity. Although this approach has been evaluated in many contexts, the question remains if there are further opportunities to improve test focusing. One idea is to identify other types of information that may indicate the location of defect-prone software parts. Data from software inspections, in particular, appear to be promising. This kind of data might already lead to software parts that have inherent difficulties or programming challenges, and in consequence might be defect-prone. This article first explains how inspection and product metrics can be used to focus testing activities. Second, we compare selected product and inspection metrics commonly used to predict defect-prone parts (e.g. size and complexity metrics, inspection defect content metrics, and defect density metrics). Based on initial experience from two case studies performed in different environments, the suitability of different metrics for predicting defect-prone parts is illustrated. The studies revealed that inspection defect data seems to be a suitable predictor, and a combination of certain inspection and product metrics led to the best prioritizations in our contexts. In addition, qualitative experience is presented, which substantiates the expected benefit of using inspection results to optimize testing.
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JI, Haijin, Song HUANG, Xuewei LV, Yaning WU, and Yuntian FENG. "Empirical Studies of a Kernel Density Estimation Based Naive Bayes Method for Software Defect Prediction." IEICE Transactions on Information and Systems E102.D, no. 1 (2019): 75–84. http://dx.doi.org/10.1587/transinf.2018edp7177.
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López-Martín, Cuauhtémoc, Yenny Villuendas-Rey, Mohammad Azzeh, Ali Bou Nassif, and Shadi Banitaan. "Transformed k-nearest neighborhood output distance minimization for predicting the defect density of software projects." Journal of Systems and Software 167 (September 2020): 110592. http://dx.doi.org/10.1016/j.jss.2020.110592.
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Ren, Shengbing, Wanying Zhang, Hafiz Shahbaz Munir, and Lei Xia. "Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction." Algorithms 12, no. 1 (2019): 13. http://dx.doi.org/10.3390/a12010013.
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Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods use feature attributes to represent samples, which cannot avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP). This method not only retains the original information, but also obtains the relationship with other objects. So it can enhances the discriminant ability of the sample attributes to the class label. This method firstly uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In this space, the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the k-Nearest Neighbor (KNN) algorithm is used to label those samples. Finally, the model is learned from training data based on TrAdaBoost method and used to predict new potential defects. The experimental results show that this approach has better performance than other traditional CPDP methods.
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Li, Jian, and Bin Ting Yang. "Influence of Processing Parameters of Warm Compaction on Green Density of Molybdenum Powder and Compact Defect Analysis." Advanced Materials Research 314-316 (August 2011): 524–29. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.524.
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Utilization of MSC.Marc FEM software, the typical warm compaction process of molybdeum powder was simulated. Influence of processing parameters of warm compaction on green density of molybdenum powder were studied. Furthermore, the commonly defect existing in green body were analyzed. The results show that compaction pressure is an important factor on green density. At the beginning of warm compaction, average relative density rises linearly along with the increasing pressure. The green density increases with the suppression speed increasing, when velocity value reaches 7mm/s the density will not increase anymore. After the value of friction coefficient is greater than 0.1,green density decreases with the friction coefficient increasing. Influence of temperature on compaction is improvement of the lubrication condition, and reduces the friction coefficient. The appearance of axial tensile stress is the important factor which causes delamination and cracks. Internal friction and lubrication condition of powder mixed system is the important reason results in defect.
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Lin, Jinfu, Shulong Wang, Hongxia Liu, Kai Yin, Lei Wu, and Wei Li. "The Effect of Ion Irradiation Density on the Defect of Graphene: A Molecular Dynamics Study." Crystals 10, no. 3 (2020): 158. http://dx.doi.org/10.3390/cryst10030158.
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This paper analyzes graphene irradiation effects using a molecular dynamic simulation software, large-scale atomic / molecular massively parallel simulator (LAMMPS). We propose a hypothesis for the effective range of incident ions based on simulation results of irradiation effects that were found using a suspended single-layer graphene. This explains the influence mechanism of irradiation density on the degree of material defects. This paper does key research on how copper substrate influences a single- and bi-layer graphene. The results show that for a single-layer graphene (SLG) the substrate increases the effective range of the incident ions. Within a certain range of the irradiation density, the substrate enhances the defect production on graphene in low-energy irradiation (<5 keV). However, due to the shielding effect of the substrate, the overall trend of graphene damage will be reduced. For the bi-layer graphene (BLG), the effect of the indirect action range is more obvious than that of the direct-action range. In the case of low irradiation density, the knock-on atoms of BLG are much less than suspended SLG.
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Yang, Xingguang, Huiqun Yu, Guisheng Fan, and Kang Yang. "DEJIT: A Differential Evolution Algorithm for Effort-Aware Just-in-Time Software Defect Prediction." International Journal of Software Engineering and Knowledge Engineering 31, no. 03 (2021): 289–310. http://dx.doi.org/10.1142/s0218194021500108.
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Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. The effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify defective software changes in limited software testing resources. Although many methods have been proposed to solve the JIT-SDP, the effort-aware prediction performance of the existing models still needs to be further improved. To this end, we propose a differential evolution (DE) based supervised method DEJIT to build JIT-SDP models. Specifically, first we propose a metric called density-percentile-average (DPA), which is used as optimization objective on the training set. Then, we use logistic regression (LR) to build a prediction model. To make the LR obtain the maximum DPA on the training set, we use the DE algorithm to determine the coefficients of the LR. The experiment uses defect data sets from six open source projects. We compare the proposed method with state-of-the-art four supervised models and four unsupervised models in cross-validation, cross-project-validation and timewise-cross-validation scenarios. The empirical results demonstrate that the DEJIT method can significantly improve the effort-aware prediction performance in the three evaluation scenarios. Therefore, the DEJIT method is promising for the effort-aware JIT-SDP.
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Zu, Qi Rui, Jing Bai, Xiao Shu Wang, et al. "Defect Stability and Electronic Configuration of Off-Stoichiometric Ni-X-In (X = Mn, Fe and Co) Alloys: A First-Principles Study." Materials Science Forum 873 (September 2016): 8–12. http://dx.doi.org/10.4028/www.scientific.net/msf.873.8.
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Ni-Mn-In is a novel type of magnetic shape memory alloy, its shape memory effect has been realized through magnetic field induced reverse martensitic transformation. A variety of point defects would be generated during composition adjustment process, such as antisite defect, vacancy and exchange. The first–principles calculations within the framework of the density functional theory using the Vienna ab initio software package (VASP) have been used in this paper to investigate the defect formation energy and electronic configuration of the off-stoichiometric Ni-X-In (X= Mn, Fe and Co) alloys. The In antisite on the X sublattice (InX) and the Ni antisite on the X sublattice (NiX) have the lowest formation energies in the investigated series. The formation energy of the Ni vacancy is the lowest, while that of the in vacancy is the highest. It is confirmed that the in constituent plays a dominant role for stabilizing the austenitic phase.
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Vimaladevi M. and Zayaraz G. "A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks." Journal of Cases on Information Technology 22, no. 3 (2020): 1–18. http://dx.doi.org/10.4018/jcit.2020070101.
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The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.
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Manoua, Mohamed, Tariq Jannane, Otmane Abouelala, et al. "Modeling and optimization of n-ZnO/p-Si heterojunction using 2-dimensional numerical simulation." European Physical Journal Applied Physics 90, no. 1 (2020): 10101. http://dx.doi.org/10.1051/epjap/2020190333.
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In this work, n-ZnO/p-Si heterojunction was investigated using two-dimensional numerical simulation. The effect of Zinc Oxide thickness, carrier concentration in Zinc Oxide layer, minority carrier lifetime of bulk Silicon and the interface states density on electrical properties were studied in dark and under illumination conditions. This study aimed to optimize these parameters in order to obtain n-ZnO/p-Si solar cell with high conversion efficiency and low cost. The simulation was carried out by Atlas silvaco software. As results, a very low saturation current Is, low series resistance Rs, an ideality factor n between 1 and 1.5 were obtained for optimal charge carrier concentrations in the range [5 × 1019–5 × 1021 cm−3] and a thickness of Zinc Oxide between 0.6 and 2 µm. Moreover, a photovoltaic conversion efficiency of 24.75% was achieved without interfacial defect, which decreases to 5.49% for an interface defect density of 5 × 1014 cm−2.
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Zhao, Wei, Yu Yan, Hai Bo Wang, and Jin Feng Gao. "Finite Element Analysis and Fracture Forecast of U Channel Flexible Roll Forming." Advanced Materials Research 683 (April 2013): 604–7. http://dx.doi.org/10.4028/www.scientific.net/amr.683.604.
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Focus on fracture defect of DP980 in Flexible roll forming, this research have done a finite-element analysis based on explicit solver and shear damage criterion, and using element deletion of finite element software ABAQUS to realize the fracture evolution of sheet damaged area. During the modeling, parameters of fracture evolution are set, and finally gain the conclusion about the relationship between stress, strain, energy density and fracture. The research provides theoretical guidance for practical production.
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Джафарова, В. Н., Г. С. Оруджев, С. С. Гусейнова, В. Р. Стемпицкий та М. С. Баранaва. "Магнитные свойства вакансий и внедренного хрома в кристалле ZnO". Физика и техника полупроводников 52, № 8 (2018): 916. http://dx.doi.org/10.21883/ftp.2018.08.46219.8678.
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AbstractThe electronic and magnetic properties of ZnO containing Cr doped atoms and Zn and O vacancies in its crystal structure are theoretically investigated. Calculations are performed using Atomistix Tool Kit and Vienna Ab-initio Simulation Package software implementing the electron density functional theory method with the Hubbard correction. It is shown that the magnetic moment of a defect supercell strongly depends on the impurity concentration and presence of vacancies. The doping of an oxygen atom increases the probability of zinc-vacancy formation.
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Quesada-López, Christian, Erika Hernandez-Agüero, and Marcelo Jenkins. "Characterization of software testing practices: A replicated survey in Costa Rica." Journal of Software Engineering Research and Development 7 (December 19, 2019): 6. http://dx.doi.org/10.5753/jserd.2019.472.
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Software testing is an essential activity in software development projects for delivering high quality products. In a previous study, we reported the results of a survey of software engineering practices in the Costa Rican industry. To make a more in depth analysis of the specific software testing practices among practitioners, we replicated a previous survey conducted in South America. Our objective was to characterize the state of the practice based on practitioners use and perceived importance of those practices. This survey evaluated 42 testing practices grouped in three categories: processes, activities and tools. A total of 92 practitioners responded to the survey. The participants indicated that: (1) tasks for recording of the results of tests, documentation of test procedures and cases, and re-execution of tests when the software is modified are useful and important for software testing practitioners. (2) Acceptance and system testing are the two most useful and important testing types. (3) Tools for recording defects and the effort to fix them (bug tracking) and the availability of a test database for reuse are useful and important. Regarding use and implementation of practices, the participants stated that (4) Planning and designing of software testing before coding and evaluating the quality of test artifacts are not a regular practice. (5) There is a lack of measurement of defect density and test coverage in the industry; and (6) tools for automatic generation of test cases and for estimating testing effort are rarely used. This study gave us a first glance at the state of the practice in software testing in a thriving and very dynamic industry that currently employs most of our computer science professionals. The benefits are twofold: for academia, it provides us with a road map to revise our academic offer, and for practitioners it provides them with a first set of data to benchmark their practices.
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Min, Jie, Hai Sheng Wang, De Wei Guo, and Wen Bin Zhang. "Stretching and Chamfering Finite Element Analysis of Rotor Loose Compaction Based on DEFORM." Applied Mechanics and Materials 543-547 (March 2014): 3–6. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3.
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DEFORM is a software used for FEA (Finite Element Analysis) simulation. By using this software, I take a research on the procedure when a steel billet with defect of artificial loosening is drawn out in a simulated environment. Then I build a FEA model about the loosening and compaction of a large-sized rotor and stimulate the procedure in accordance with current craft card involving rotor forging. Finally, I get a result: the relative density of the loose area reaches up to 85% after the first drawing-out process (note: forging ratio 1.47). After simulating the procedure of chamfering on a billet which has been already drawn out, I found that chamfering had little substantial impact on the its loosening and compaction.
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MOHAN, K. KRISHNA, A. SRIVIDYA, and RAVI KUMAR GEDELA. "QUALITY OF SERVICE PREDICTION USING FUZZY LOGIC AND RUP IMPLEMENTATION FOR PROCESS ORIENTED DEVELOPMENT." International Journal of Reliability, Quality and Safety Engineering 15, no. 02 (2008): 143–57. http://dx.doi.org/10.1142/s021853930800299x.
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In a competitive business landscape, large organizations such as insurance companies and banks are under high pressure to innovate, improvise and differentiate their products and services while continuing to reduce the time-to market for new product introductions. Generating a single view of the customer is critical from different perspectives of the systems developer over a period of time because of the existence of disconnected systems within an enterprise. Therefore, to increase revenues and cost optimization, it is important build enterprise systems more closely with the business requirements by reusing the existing systems. While building distributed based applications, it is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate the risks and increase the reliability of systems. Experiences in developing applications in Java Enterprise Edition (JEE) with customized RUP have been presented in this paper. RUP is adopted into an onsite-offshore development model along with ISO 9001 and SEI CMM Level 5 standards. This paper provides an RUP approach to achieve increased reliability with higher productivity and lower defect density along with competitiveness through cost effective custom software solutions. Early qualitative software reliability prediction is done using fuzzy expert systems, using which the expected number of defects in the software prior to the experimental testing is obtained. The predicted results are then compared with the practical values obtained during the actual testing procedure.
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Мясникова, А. С., та А. И. Богданов. "Влияние ионов In-=SUP=-3+-=/SUP=- и Ga-=SUP=-3+-=/SUP=- на запрещенную зону кристаллов щелочно-земельных фторидов: неэмпирический расчет". Журнал технической физики 126, № 4 (2019): 428. http://dx.doi.org/10.21883/os.2019.04.47511.331-18.
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AbstractThe results of nonempirical quantum chemical calculations of CaF_2, SrF_2, and BaF_2 crystals that were activated by In^3+ and Ga^3+ ions have been presented. The calculations were performed in the framework of density functional theory using the VASP software complex. The estimation of the width of the band gap of defect-free crystals have been carried out by different methods and the influence of impurity ions on the band gap width has been estimated as well as the possibility of getting rid of shallow traps by introducing an impurity of indium or gallium has been investigated.
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de Freitas Costa, Neusa Motta, Bruno Raposo Melo, Rosana Tavares Brito, et al. "Techniques to Study Cellular Response in Critical Size Bone Defect Healing on Rat Calvaria Treated with Hydroxyapatite Implants." Key Engineering Materials 396-398 (October 2008): 285–88. http://dx.doi.org/10.4028/www.scientific.net/kem.396-398.285.
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The aim of this paper was to evaluate the usefulness of coupling digital image analysis with immunohistochemistry and histomorphometry data to the study of tissue response to hydroxyapatite in a model of critical size bone defect in calvaria of rats. A transosseous defect measuring 8 mm in diameter was performed with a surgical trephine in the parietal bone of 40 rats and divided into two experimental groups according to the treatment: group I (blood clot, control), group II (HA) and killed 1, 3, 6 and 9 months after implantation (n=5/group/period). The skullcaps with overlaying skin were collected and processed for paraffin embedding. The specimens were cut in the laterolateral direction into 5-µm thick semi-serial sections and stained with hematoxylin-eosin for identification and counting of polymorphonuclears cells, mastocytes, and multinucleated giant cells, MNG, or immunolabeled with anti- lysozyme, -factor VIII and –PCNA. Digital images were obtained and analyzed with the ImagePro-Plus® software for cell couting (polymorphonuclears cells, mastocytes, macrophages and MNG) and microvessel density. Image segmentation of anti-PCNA immunostaining was used for cell proliferation analysis. The digital images obtained allowed clear identification of cells of interest by through morphological aspects or immunostaining. Data recording and analysis was facilitated by the use of specific software for image processing and graphical and statistical analysis. It can be concluded that the techniques applied were usefull to identify and count cells, structures and process of interest making easier the effectiveness of hydroxyapatite in the critical size defect in rat calvaria model.
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Zhang, Yili, and Güneş Koru. "Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions." Journal of the American Medical Informatics Association 27, no. 3 (2019): 386–95. http://dx.doi.org/10.1093/jamia/ocz201.
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Abstract Objective Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States. Materials and Methods There were more than 2.23 million rows and 32 million cells in the Medicaid data examined. The taxonomy was developed through document review, descriptive data analysis, and literature review. A software program was created to automatically detect defects by using a set of constraints whose development was facilitated by the taxonomy. Results Five major categories and seventeen subcategories of defects were identified. The major categories are missingness, incorrectness, syntax violation, semantic violation, and duplicity. More than 3 million defects were detected indicating substantial problems with data quality. Defect density exceeded 10% in five tables. The majority of the data defects belonged to format mismatch, invalid code, dependency-contract violation, and implausible value types. Such contextual knowledge can support prioritized quality improvement initiatives for the Medicaid data studied. Conclusions This research took the initial steps to understand the types of data defects and detect defects in large healthcare datasets. The results generally suggest that healthcare organizations can potentially benefit from focusing on data quality improvement. For those purposes, the taxonomy developed and the approach followed in this study can be adopted.
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Bazlov, V. A., T. Z. Mamuladze, O. I. Golenkov, et al. "Effects of 3D Imaging on Surgical Tactics in Primary and Revision Hip Arthroplasty." Traumatology and Orthopedics of Russia 26, no. 2 (2020): 60–70. http://dx.doi.org/10.21823/2311-2905-2020-26-2-60-70.
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3D imaging tools significantly expand the ability to assess the bone tissue condition, both in terms of its qualitative properties and in terms of accurate determination of bone defect geometry and volume.The purpose of the study was to determine the 3D imaging potential for the preoperative planning and correction of surgical tactics in hip arthroplasty.Materials and Methods. A retrospective analysis of the preoperative planning of 110 primary and revision hip arthroplasties with 3D imaging was performed. The following specialized software were employed: RadiAnt DICOM Viewer file converter — for 3D models production; 3D/CAD designers — for volumetric models processing and correction; InVesalius 3.0 program — for bone density evaluation by the Hounsfield scale; K-Pacs — for viewing MSCT and X-ray images. All patients underwent pelvic bones radiography in the front and anterior-lateral planes. Post-traumatic acetabular deformity was described in accordance with the X-ray picture in each individual clinical case. For revision arthroplasty, the acetabular defect was determined according to the W.G. Paprosky classification. In 36 patients (32.7%), the acetabulum defect was the result of trauma. In 74 patients (67.3%), the cause of surgery was endoprosthesis components loosening.Results. In 80% of cases (88 patients), the analysis of the 3D model did not change the surgical tactics determined in the preoperative planning using pelvic radiographs; in 20% of cases (22 patients), the use of 3D imaging revealed new circumstances and changed the surgical tactics.Conclusion. In standard cases, it is possible to use the traditional preoperative planning using radiographs in several planes. In primary hip arthroplasty in the patients with post-traumatic deformity, including a false joint of acetabulum bottom or 2 to 3 degree osteopenia, it is advisable to perform 3D imaging. In the case of revision arthroplasty, 3D visualization is indicated in acetabulum Paprosky IIIA, IIIB defects with pelvic discontinuity.
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Abega, F. X. Abomo, A. Teyou Ngoupo, and J. M. B. Ndjaka. "Numerical Design of Ultrathin Hydrogenated Amorphous Silicon-Based Solar Cell." International Journal of Photoenergy 2021 (August 14, 2021): 1–13. http://dx.doi.org/10.1155/2021/7506837.
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Numerical modelling is used to confirm experimental and theoretical work. The aim of this work is to present how to simulate ultrathin hydrogenated amorphous silicon- (a-Si:H-) based solar cells with a ITO BRL in their architectures. The results obtained in this study come from SCAPS-1D software. In the first step, the comparison between the J-V characteristics of simulation and experiment of the ultrathin a-Si:H-based solar cell is in agreement. Secondly, to explore the impact of certain properties of the solar cell, investigations focus on the study of the influence of the intrinsic layer and the buffer layer/absorber interface on the electrical parameters ( J SC , V OC , FF, and η ). The increase of the intrinsic layer thickness improves performance, while the bulk defect density of the intrinsic layer and the surface defect density of the buffer layer/ i -(a-Si:H) interface, respectively, in the ranges [109 cm-3, 1015 cm-3] and [1010 cm-2, 5 × 10 13 cm-2], do not affect the performance of the ultrathin a-Si:H-based solar cell. Analysis also shows that with approximately 1 μm thickness of the intrinsic layer, the optimum conversion efficiency is 12.71% ( J SC = 18.95 mA · c m − 2 , V OC = 0.973 V , and FF = 68.95 % ). This work presents a contribution to improving the performance of a-Si-based solar cells.
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Tawfik, Ahmed, Mohamed Radwan, Mazen Ahmed Attia, Paul Bills, Radu Racasan, and Liam Blunt. "The Detection of Unfused Powder in EBM and SLM Additive Manufactured Components." International Journal of Automation Technology 14, no. 6 (2020): 1025–35. http://dx.doi.org/10.20965/ijat.2020.p1025.
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Additive manufacturing (AM) is recognized as a core technology for producing high value, complex, and individually designed components as well as prototypes, giving AM a significant advantage over subtractive machining. Selective laser melting (SLM) or electron beam melting (EBM) are two of the main technologies used for producing metal components. The powder size varies, depending on the technology and manufacturer, from 20–50 μm for SLM and 45–100 μm for EBM. One of the current barriers for implementing AM for most industries is the lack of build repeatability and a deficit in quality assurance standards. The mechanical properties of the components depend critically on the density achieved; therefore, defect analysis and detection of unfused powder must be carried out to verify the integrity of the components. Detecting unfused powder in AM parts using X-ray computed tomography (XCT) is challenging because detection relies on variations in density. Unfused particles have the same density as the manufactured parts; therefore, detection is difficult using standard methods for density measurement. This study presents a methodology to detect unfused powders in SLM and EBM-manufactured components. Aluminum and titanium artefacts with designed internal defects filled with unfused powder are scanned with XCT and the results are analyzed with VGSTUDIO Max 3.0 (Volume Graphics, Germany) software package. Preliminary results indicate that detecting unfused powder in an aluminum SLM artifact with a 9.5 μm voxel size is achievable. This is possible because of the size of the voids between the powder particles and the non-uniform shape of the particles. Conversely, detecting unfused powder in the EBM-manufactured titanium artifact is less challenging owing to the uniform spherical shape and slightly larger size of the particles.
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Wagdargi, Shivaraj S., Kirthi Kumar Rai, KV Arunkumar, Basavraj Katkol, and Gururaj Arakeri. "Evaluation of Spontaneous Bone Regeneration after Enucleation of Large Cysts of the Jaws using Radiographic Computed Software." Journal of Contemporary Dental Practice 17, no. 6 (2016): 489–95. http://dx.doi.org/10.5005/jp-journals-10024-1878.
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ABSTRACT Introduction Spontaneous regeneration of bone is commonly seen in the small surgical defects caused by enucleation of cysts. However, in case of large surgical defects caused by the enucleation, spontaneous regeneration of bone is a rare phenomenon and it depends on factors, such as age of the patient, intact periosteum, and proper stabilization. Materials and methods The study included 16 patients, who reported to the department of oral and maxillofacial surgery with the complaint of pain and swelling in the jaws diagnosed as cyst. The sample included equal numbers of male and female subjects aged between 15 and 40 years. Panoramic radiographs were taken pre- and postoperatively on day 2 of the enucleation. The dimensions of the cyst were evaluated on the radiograph according to the proforma. Subsequent radiographs were taken at regular intervals of 1.5, 3, and 6 months using standard parameters and were analyzed using MCID™ analysis software of imaging research. Results Mean reduction was seen in up to 39 and 60% in the cystic cavity size and increase in the mean density up to 59 and 90.2% at 3 and 6 months intervals respectively. Conclusion Spontaneous bone regeneration was seen even after primary closure of the large cystic defect without the need for placement of foreign substances or grafts and it also eliminated the complications resulting from placement of foreign substance. Further studies are required in a larger sample with longer follow-up durations to confirm the outcome of the present work for the benefit of patients. Clinical significance The present study depicted that spontaneous bone regeneration can occur with accepted results after simple enucleation of jaw cyst without the aid of any graft material. Hence, simple enucleation may be considered as a first line of treatment modality for cystic lesion of the jaws. This simplifies the surgical procedure, decreases the economic and biologic costs, and reduces the risk of postoperative complications. Follow-up is necessary along with patient's compliance for the success of treatment. How to cite this article Wagdargi SS, Rai KK, Arunkumar KV, Katkol B, Arakeri G. Evaluation of Spontaneous Bone Regeneration after Enucleation of Large Cysts of the Jaws using Radiographic Computed Software. J Contemp Dent Pract 2016;17(6):489-495.
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Trautsch, Alexander, Steffen Herbold, and Jens Grabowski. "A longitudinal study of static analysis warning evolution and the effects of PMD on software quality in Apache open source projects." Empirical Software Engineering 25, no. 6 (2020): 5137–92. http://dx.doi.org/10.1007/s10664-020-09880-1.
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Abstract Automated static analysis tools (ASATs) have become a major part of the software development workflow. Acting on the generated warnings, i.e., changing the code indicated in the warning, should be part of, at latest, the code review phase. Despite this being a best practice in software development, there is still a lack of empirical research regarding the usage of ASATs in the wild. In this work, we want to study ASAT warning trends in software via the example of PMD as an ASAT and its usage in open source projects. We analyzed the commit history of 54 projects (with 112,266 commits in total), taking into account 193 PMD rules and 61 PMD releases. We investigate trends of ASAT warnings over up to 17 years for the selected study subjects regarding changes of warning types, short and long term impact of ASAT use, and changes in warning severities. We found that large global changes in ASAT warnings are mostly due to coding style changes regarding braces and naming conventions. We also found that, surprisingly, the influence of the presence of PMD in the build process of the project on warning removal trends for the number of warnings per lines of code is small and not statistically significant. Regardless, if we consider defect density as a proxy for external quality, we see a positive effect if PMD is present in the build configuration of our study subjects.
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Nawaz, Muhammad. "Design Analysis of a-Si/c-Si HIT Solar Cells." Advances in Science and Technology 74 (October 2010): 131–36. http://dx.doi.org/10.4028/www.scientific.net/ast.74.131.
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A theoretical design analysis using numerical two dimensional computer aided design tool (i.e., TCAD) is presented for a-Si/c-Si based heterojunction (HJ) solar cells. A set of optical beam propagation models, complex refractive index models and defect models for a-Si material implemented (in-built) in the simulation software are first evaluated for single (SHJ) and double heterojunction (DHJ) devices. Assessment is further carried out by varying physical parameters of the layer structures such as doping, thickness of the c-Si and a-Si layers, defect density in the a-Si layer and bandgap discontinuity parameter. With varying bandgap discontinuity and using standard transport model in numerical device simulation, HJ solar cell performance is undervalued (η = 19.5%). This is the result of poor photogenerated carrier collection due to the presence of heterojunction at the respective n and p-contacts of the device. Implementing thermionic field emission tunneling model at the heterojunction, we obtained improved performance (η = 24 %) over large range of bandgap discontinuities. Keeping improved efficiency of HJ cell, implementing a step graded a-Si layer, further helps to widen the range of bandgap discontinuity parameter.
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Bauer, Sondes, Sergey Lazarev, Martin Bauer, et al. "Three-dimensional reciprocal space mapping with a two-dimensional detector as a low-latency tool for investigating the influence of growth parameters on defects in semipolar GaN." Journal of Applied Crystallography 48, no. 4 (2015): 1000–1010. http://dx.doi.org/10.1107/s1600576715009085.
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A rapid nondestructive defect assessment and quantification method based on X-ray diffraction and three-dimensional reciprocal-space mapping has been established. A fast read-out two-dimensional detector with a high dynamic range of 20 bits, in combination with a powerful data analysis software package, is set up to provide fast feedback to crystal growers with the goal of supporting the development of reduced defect density GaN growth techniques. This would contribute strongly to the improvement of the crystal quality of epitaxial structures and therefore of optoelectronic properties. The method of normalized three-dimensional reciprocal-space mapping is found to be a reliable tool which shows clearly the influence of the parameters of the metal–organic vapour phase epitaxial and hydride vapour phase epitaxial (HVPE) growth methods on the extent of the diffuse scattering streak. This method enables determination of the basal stacking faults and an exploration of the presence of other types of defect such as partial dislocations and prismatic stacking faults. Three-dimensional reciprocal-space mapping is specifically used in the manuscript to determine basal stacking faults quantitatively and to discuss the presence of partial dislocations. This newly developed method has been applied to semipolar GaN structures grown on patterned sapphire substrates (PSSs). The fitting of the diffuse scattering intensity profiles along the stacking fault streaks with simulations based on a Monte Carlo approach has delivered an accurate determination of the basal plane stacking fault density. Three-dimensional reciprocal-space mapping is shown to be a method sensitive to the influence of crystallographic surface orientation on basal stacking fault densities during investigation of semipolar (11{\overline 2}2) GaN grown on anr-plane (1{\overline 1}02) PSS and semipolar (10{\overline 1}1) GaN grown on ann-plane (11{\overline 2}3) PSS. Moreover, the influence of HVPE overgrowth at reduced temperature on the quality of semipolar (11{\overline 2}2) GaN has been studied.
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Wanaguru, Prabath, Asok K. Ray, and Qiming Zhang. "Influence of H, H2, O and O2 on Armchair SiGe Nanotubes: A Theoretical Study." Journal of Nanoscience and Nanotechnology 16, no. 4 (2016): 3447–56. http://dx.doi.org/10.1166/jnn.2016.12342.
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A systematic, hybrid density functional theory study of interaction between SiGe nanotubes (SiGeNTs) and X (X = H, O, H2 and O2) have been performed using the hybrid functional B3LYP and an all electron 3-21G* basis set implemented in GAUSSIAN 09 suite of software. All possible internal and external adsorption sites were considered, and it was found that H prefers to move onto top of an atom site while O prefers to incorporate into NT wall by breaking the bonds. Adsorption energies for H is ∼2.0 eV and for O it is ∼5.0 eV. Controlled adsorption of atomic H and several molecular O give rises to defect density states in the frontier orbital region. H rich adsorptions predict the difference between highest occupied molecular orbital (HOMO) energy and the lowest unoccupied molecular orbital (LUMO) energy increase while O rich adsorptions predict the decrease in HOMO-LUMO energy gap. O and O2 adsorptions predict definite ionic bonding character while H atomic adsorptions predict covalent bonding. H2 is very neutral towards the adsorption into SiGeNTs and clearly shows the physisorption adsorption. Considering the all adsorptions, the adsorptions happened within the Si vicinity of the SiGeNT shows the most stable and preferred adsorption region.
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Kanaoka, Hideaki, Soshu Kirihara, and Yoshinari Miyamoto. "Terahertz wave properties of alumina microphotonic crystals with a diamond structure." Journal of Materials Research 23, no. 4 (2008): 1036–41. http://dx.doi.org/10.1557/jmr.2008.0122.
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Fabrication and terahertz wave properties of alumina microphotonic crystals with a diamond structure were investigated. The three-dimensional diamond structure was designed on a computer using 3D-CAD software. The designed lattice constant was 500 μm. The structure consisted of 8 × 8 × 4 unit cells. Acrylic diamond structures with an alumina dispersion of 40 vol% were formed by using microstereolithography. Fabricated precursors were dewaxed at 600 °C and sintered at 1500 °C. The linear shrinkage ratio was about 25%. The relative density reached 97.5%. The electromagnetic wave properties were measured by terahertz time-domain spectroscopy. A complete photonic band gap was observed at the frequency range from 0.40 THz to 0.47 THz, and showed good agreement with the simulation results calculated by the plane wave expansion method. Moreover, localized modes were obtained at the frequencies 0.42 THz and 0.46 THz by introducing an air defect in the diamond structure. They corresponded to the simulation by the transmission line modeling method.
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Tawfik, Ahmed, Paul Bills, Liam Blunt, and Radu Racasan. "Development of an Additive Manufactured Artifact to Characterize Unfused Powder Using Computed Tomography." International Journal of Automation Technology 14, no. 3 (2020): 439–46. http://dx.doi.org/10.20965/ijat.2020.p0439.
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Additive manufacturing (AM) is recognized as a core technology for producing high-value components. The production of complex and individually modified components, as well as prototypes, gives additive manufacturing a substantial advantage over conventional subtractive machining. For most industries, some of the current barriers to implementing AM include the lack of build repeatability and a deficit of quality assurance standards. The mechanical properties of the components depend critically on the density achieved. Therefore, defect/porosity analysis must be carried out to verify the components’ integrity and viability. In parts produced using AM, the detection of unfused powder using computed tomography is challenging because the detection relies on differences in density. This study presents an optimized methodology for differentiating between unfused powder and voids in additive manufactured components, using computed tomography. Detecting the unfused powder requires detecting the cavities between particles. Previous studies have found that the detection of unfused powder requires a voxel size that is as small as 4 μm3. For most applications, scanning using a small voxel size is not reasonable because of the part size, long scan time, and data analysis. In this study, different voxel sizes are used to compare the time required for scanning, and the data analysis showing the impact of voxel size on the detection of micro defects. The powder used was Ti6Al4V, which has a grain size of 45–100 μm, and is typically employed by Arcam electron beam melting (EBM) machines. The artifact consisted of a 6 mm round bar with designed internal features ranging from 50 μm to 1400 μm and containing a mixture of voids and unfused powder. The diameter and depth of the defects were characterized using a focus variation microscope, after which they were scanned using a Nikon XTH225 industrial CT to measure the artifacts and characterize the internal features for defects/pores. To reduce the number of the process variables, the measurement parameters, such as filament current, acceleration voltage, and X-ray filtering material and thickness were kept constant. The VGStudio MAX 3.0 (Volume Graphics, Germany) software package was used for data processing, surface determination, and defects/porosity analysis. The main focus of this study is to explore the optimal methods for enhancing the detection of pores/defects while minimizing the time taken for scanning, data analysis, and determining the effects of noise on the analysis.
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Kun Peng, Wen, and Sung Young Park. "Automotive CVT sheave development using complex forging processes." International Journal of Engineering & Technology 7, no. 3.3 (2018): 329. http://dx.doi.org/10.14419/ijet.v7i2.33.14179.
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Background/Objectives: In this study, we developed a complex forging process to produce a sheave, which is a core part of CVT for automobiles, fabricated prototypes. The existing processes such as hot forging and the MCT machining process have disadvantages including excessive amounts of material cutting, number of processes, and processing time.Methods/Statistical analysis: To overcome these shortcomings, in this study, a shape was created using hot forging and the shape was molded into a precision part using cold forging. A complex forging process was developed that enabled maximization of the metallographic density and reduction of material cutting quantity by applying a minimal number of processes. By conducting heat compression and tensile tests of the samples, we identified the physical properties of the material and used the commercial software Deform-3D to conduct a forging analysis.Findings: Based on the analysis results, we designed a combined process of hot forging and cold forging and fabricated a mold and pilot products. The fabricated pilot products were evaluated using a metal flow test, an internal defect test, a surface roughness test, etc. According to the evaluation results, no significant defect was observed, and we therefore believe it is adequate for mass production.Improvements/Applications: The complex forging method can reduce the use of material by approximately 15% compared with the hot forging and the MCT machining process. Through this study, we established a product development process using a high-precision complex forging technology.
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PAN, SHENG, AURELIE AZOUG, and JAMES K. GOOD. "Viscoelastic web curl due to storage in wound rolls." July 2020 19, no. 7 (2020): 357–67. http://dx.doi.org/10.32964/tj19.7.357.
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Winding is often the final operation in a roll-to-roll manufacturing process. Web materials, i.e., materials that are thin compared to their length, are wound into rolls because this form is the only practical means to store them. The resulting bending strains and associated stresses are large for thick webs and laminates. As many webs are viscoelastic on some time scale, bending stresses lead to creep and inhomogeneous changes in length. When the web material is unwound and cut into discrete samples, a residual curvature remains. This curvature, called curl, is the inability for the web to lie flat at no tension. Curl is an undesirable web defect that causes loss of productivity in a subsequent web process. This paper describes the development and implementation of modeling and experimental tools to explore and mitigate curl in homogenous webs. Two theoretical and numerical methods that allow the prediction of curl in a web are developed: a winding software based on bending recovery theory, and the implementation of dynamic simulations of winding. One experimental method is developed that directly measures the curl online by taking advantage of the anticlastic bending resulting from the curl. These methods are demonstrated for a low-density polyethylene web.