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Добірка наукової літератури з теми "Defect prediction model"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Defect prediction model"

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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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Анотація:
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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2

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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Анотація:
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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Анотація:
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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4

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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Анотація:
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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Анотація:
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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6

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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Анотація:
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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Анотація:
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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Анотація:
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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9

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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Анотація:
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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10

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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Анотація:
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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Більше джерел

Дисертації з теми "Defect prediction model"

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Tran, Qui Can Cuong. "Empirical evaluation of defect identification indicators and defect prediction models." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2553.

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Анотація:
Context. Quality assurance plays a vital role in the software engineering development process. It can be considered as one of the activities, to observe the execution of software project to validate if it behaves as expected or not. Quality assurance activities contribute to the success of software project by reducing the risks of software’s quality. Accurate planning, launching and controlling quality assurance activities on time can help to improve the performance of software projects. However, quality assurance activities also consume time and cost. One of the reasons is that they may not focus on the potential defect-prone area. In some of the latest and more accurate findings, researchers suggested that quality assurance activities should focus on the scope that may have the potential of defect; and defect predictors should be used to support them in order to save time and cost. Many available models recommend that the project’s history information be used as defect indicator to predict the number of defects in the software project. Objectives. In this thesis, new models are defined to predict the number of defects in the classes of single software systems. In addition, the new models are built based on the combination of product metrics as defect predictors. Methods. In the systematic review a number of article sources are used, including IEEE Xplore, ACM Digital Library, and Springer Link, in order to find the existing models related to the topic. In this context, open source projects are used as training sets to extract information about occurred defects and the system evolution. The training data is then used for the definition of the prediction models. Afterwards, the defined models are applied on other systems that provide test data, so information that was not used for the training of the models; to validate the accuracy and correctness of the models Results. Two models are built. One model is built to predict the number of defects of one class. One model is built to predict whether one class contains bug or no bug.. Conclusions. The proposed models are the combination of product metrics as defect predictors that can be used either to predict the number of defects of one class or to predict if one class contains bugs or no bugs. This combination of product metrics as defect predictors can improve the accuracy of defect prediction and quality assurance activities; by giving hints on potential defect prone classes before defect search activities will be performed. Therefore, it can improve the software development and quality assurance in terms of time and cost
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2

Bowes, David Hutchinson. "Factors affecting the performance of trainable models for software defect prediction." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/10978.

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Анотація:
Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put right. Defect Prediction is an important part of Software Engineering. It allows developers to prioritise the code that needs to be inspected when trying to reduce the number of defects in code. A small change in the number of defects found will have a significant impact on the cost of producing software. Aims. The aim of this dissertation is to investigate the factors which a ect the performance of defect prediction models. Identifying the causes of variation in the way that variables are computed should help to improve the precision of defect prediction models and hence improve the cost e ectiveness of defect prediction. Methods. This dissertation is by published work. The first three papers examine variation in the independent variables (code metrics) and the dependent variable (number/location of defects). The fourth and fifth papers investigate the e ect that di erent learners and datasets have on the predictive performance of defect prediction models. The final paper investigates the reported use of di erent machine learning approaches in studies published between 2000 and 2010. Results. The first and second papers show that independent variables are sensitive to the measurement protocol used, this suggests that the way data is collected a ects the performance of defect prediction. The third paper shows that dependent variable data may be untrustworthy as there is no reliable method for labelling a unit of code as defective or not. The fourth and fifth papers show that the dataset and learner used when producing defect prediction models have an e ect on the performance of the models. The final paper shows that the approaches used by researchers to build defect prediction models is variable, with good practices being ignored in many papers. Conclusions. The measurement protocols for independent and dependent variables used for defect prediction need to be clearly described so that results can be compared like with like. It is possible that the predictive results of one research group have a higher performance value than another research group because of the way that they calculated the metrics rather than the method of building the model used to predict the defect prone modules. The machine learning approaches used by researchers need to be clearly reported in order to be able to improve the quality of defect prediction studies and allow a larger corpus of reliable results to be gathered.
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3

Hagene, Matthew Ray. "Momentum Defect Superposition Model for Predicting Depth-Averaged Velocities in Trapezoidal Channels." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/553.

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Анотація:
An analytical model based on linear superposition of momentum defects is developed for predicting depth-averaged velocity distributions in trapezoidal and rectangular channels with submerged and unsubmerged rigid cylinders (i.e., simulated vegetation). The model is founded on wake theory and there is an existing model that is similar except for using linear superposition of velocity defects. The momentum defects and velocity defects supposition models both require a criterion for deciding when wakes created by the rigid cylinders will be considered completely dissipated (cutoff criterion). Comparing the momentum defects and velocity defects models required developing a new cutoff criterion that would have an equivalent effect when applied to either model. The chosen cutoff criterion considers a wake to be completely dissipated when umax (the maximum defect caused by a cylinder) is less than or equal to 0.2 m/s. Predicted depth-averaged velocities from both models were compared to measured values. The predicted values differed from the measured values by less than 20% in general. It was concluded that the depth-averaged velocity predictions from the linear superposition of momentum defects model and the linear superposition of velocity defects model do not differ significantly and that the greatest advantage of the velocity defects model is that compared to the momentum defects model it is simpler to implement.
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4

James, Kyle. "DNA-MAP, a knowledge-based decision support system for Australian Defence Force forensic ancestry prediction." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/213211/1/Kyle_James_Thesis.pdf.

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Анотація:
Development of a Knowledge-Based Decision Support System to predict ancestry of the remains of missing World War Two soldiers in South-East Asia. By utilizing biological and historical information provided by the user, ancestry is assigned based on complex statistical analyses searching for distinctive patterns in the DNA that distinguish between the Australian and Japanese populations. Important features taken into consideration are the detection of a rare event, the effect of sample size and the impact of natural variation.
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5

Raiker, Joseph S. "Impulsivity and attention-deficit/hyperactivity disorder (ADHD) testing competing predictions from the working memory and behavioral inhibition models of ADHD." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4824.

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Анотація:
Impulsivity is a hallmark of two of the three DSM-IV ADHD subtypes and is associated with myriad adverse outcomes. Limited research, however, is available concerning the mechanisms and processes that contribute to impulsive responding by children with ADHD. The current study tested predictions from two competing models of ADHD--working memory (WM) and behavioral inhibition (BI)--to examine the extent to which ADHD-related impulsive responding was attributable to model-specific mechanisms and processes. Children with ADHD (n = 21) and typically developing children (n = 20) completed laboratory tasks that provided WM (domain-general central executive (CE), phonological/visuospatial storage/rehearsal) and BI indices (stop-signal reaction time (SSRT), stop-signal delay, mean reaction time). These indices were examined as potential mediators of ADHD-related impulsive responding on two diverse laboratory tasks used commonly to assess impulsive responding (CPT: continuous performance test; VMTS: visual match-to-sample). Bias-corrected, bootstrapped mediation analyses revealed that CE processes significantly attenuated between-group impulsivity differences, such that the initial large-magnitude impulsivity differences were no longer significant on either task after accounting for ADHD-related CE deficits. In contrast, SSRT partially mediated ADHD-related impulsive responding on the CPT but not VMTS. This partial attenuation was no longer significant after accounting for shared variance between CE and SSRT; CE continued to attenuate the ADHD-impulsivity relationship after accounting for SSRT. These findings add to the growing literature implicating CE deficits in core ADHD behavioral and functional impairments, and suggest that cognitive interventions targeting CE rather than storage/rehearsal or BI processes may hold greater promise for alleviating ADHD-related impairments.
ID: 030646239; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 41-55).
M.S.
Masters
Psychology
Sciences
Psychology Clinical
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6

Filho, Celso Luís de Oliveira. "Prognóstico das variáveis meteorológicas e da evapotranspiração de referência com o modelo de previsão do tempo GFS/NCEP." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/11/11131/tde-21082007-111326/.

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Анотація:
Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica, com antecedência de até três dias para o período seco. Para o úmido, este fato foi observado apenas para o primeiro dia de antecedência. A concordância entre os valores estimados pelo modelo e a partir da estação para o método de Penman-Monteith foi muito baixa, em conseqüência do desempenho do modelo de previsão do tempo em prognosticar o saldo de radiação e a velocidade do vento.
The performance of a numeric weather forecast model (GFS- Forecast System, former AVN - AvatioN model, National Center for Environmental Prediction-NCEP) was evaluated for predicting weather variables, like air temperature and vapor pressure deficit, net radiation and wind speed, as well as reference evapotranspiration calculated by Thornthwaite (1948) and Penman-Monteith (Allen et al., 1948) methods, by the comparison with data obtained by an automatic weather station, in Piracicaba, State of São Paulo, Brazil. Temperature and vapor pressure deficit were the variables predicted with the best accuracy, with a "very good" and "good" performance, according to the index of confidence proposed by Camargo and Sentelhas (1997), for the maximum of four and three days in advance, respectively, during the dry season. For the wet season, only vapor pressure deficit was predicted with a "good" performance of the model. The predictions of net radiation and wind speed were very poor for both seasons. As the weather forecast model predicted temperature well, ETo estimated by Thornthwaite method showed a good agreement with ETo values estimated by observed data from the weather station, with till three days in advance for the dry season. For the wet season, such agreement was observed just for one day in advance. When ETo estimated by Penman-Monteith method with data from the weather forecast model and from weather station were compared any agreement was observed, which was caused by the poor performance of the numeric weather forecast model to predict net radiation and wind speed.
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7

Pešek, Milan. "Detekce logopedických vad v řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218106.

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Анотація:
The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In the next part of the thesis there are results of testing of selected methods presented. For logopaedia speech defects recognition algorithms are used in order to extract the features MFCC and PLP. The segmentation of words to speech sounds is performed on the base of Differential Function method. The extracted features of a sound are classified into either a correct or an incorrect pronunciation class with one of tested methods of pattern recognition. To classify the features, the k-NN, SVN, ANN, and GMM methods are tested.
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8

Tubeuf, Helene. "Développement de stratégies de criblage de mutations d'épissage dans des gènes de prédisposition au cancer. Demystifying the splicing code: new bioinformatics insights for the interpretation of genetic variants A staggering number of genetic variations affect the splicing pattern of BRCA2 exon 7: validation of the predictive power of splicing-dedicated silico analyses MLH1 exon 7, an emblematic exon sensitive to intronic mutations but not to alterations of exonic splicing regulators, sheds light into the performance of SRE-dedicated bioinformatics approaches Calibration of pathogenicity of partial splicing defects: The model of BRCA2 Exon 3." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMR009.

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Le développement du séquençage de l’ADN à haut débit a grandement facilité le criblage de variations génétiques dans le génome des patients. Désormais, l’un des principaux défis de la génétique médicale n’est donc plus la détection des variations, mais leur interprétation fonctionnelle et clinique. Récemment, nous avons montré, à l’aide de tests fonctionnels basés sur l’utilisation de minigènes, que bien que le nombre de mutations d’épissage, et en particulier celles qui affectent sa régulation, est actuellement sous-estimé, l’effet de ces variations pourrait être dorénavant prédit à l’aide d’outils bioinformatiques spécifiques. Nous avons ainsi étendu l’évaluation du caractère prédictif de ces quatre nouvelles approches bioinformatiques par une étude comparative des scores générés par ces approches avec des données expérimentales obtenues pour un total d’environ 1200 variations exoniques. Nos travaux ont ainsi démontré la fiabilité de ces approches, utilisées seules ou en combinaison, et ont permis de proposer des recommandations quant à leur utilisation en tant qu’outils de filtration pour prioriser les variations à analyser dans des tests fonctionnels axés sur l’épissage. Néanmoins, une analyse mutationnelle exhaustive ciblée sur l’exon 7 de MLH1, a mis en évidence l’échec apparent de ces approches, pourtant validées par des études menées sur l’exon 7 de BRCA2, l’exon 10 de MAPT et l’exon 5 de MSH2, laissant suggérer que ces méthodes pourraient ne pas s’appliquer de manière équivalente à tous les exons et/ou à tous les gènes. En effet, nous avons montré que cet exon était doté de caractéristiques particulières, i.e. de sites d’épissage remarquablement forts, lui conférant une résistance totale aux mutations de régulation d’épissage et mettant en échec les outils de prédictions. Ces données contribuent à mieux déterminer les limitations de ces outils bioinformatiques tout en contribuant à leur amélioration. En dépit de ces avancées, l'évaluation de la pathogénicité des mutations d'épissage reste complexe, en particulier celles conduisant à des anomalies d'épissage en phase et/ou partielles. En utilisant, comme modèle d’étude, des variations à l’origine du saut partiel de l’exon 3 de BRCA2, nos résultats ont révélé que l’activité tumeur-suppressive de BRCA2 tolère une réduction substantielle du niveau d’expression, étant donné qu’un allèle produisant jusqu’à 70% de transcrit codant une protéine déficiente n’est pas nécessairement associé à un risque élevé de développer un cancer. L’ensemble de ces données a d’importantes implications dans le diagnostic moléculaire et la prise en charge des patients et de leurs apparentés, avec un bénéfice direct pour les familles évocatrices d’une prédisposition héréditaire et devrait contribuer à l’interprétation de VSI identifiées par séquençage à haut débit dans toute autre pathologie d’origine génétique
The development of high-throughput DNA sequencing has greatly facilitated the screening of genetic variations within patient genome. Henceforth, one of the main challenges in medical genetics is no longer the detection of variations, but their functional and clinical interpretation. Recently, we showed by using splicing reporter minigene assays, that although splicing mutations, and in particular those affecting its regulation, are more prevalent than initially estimated, they could now be predicted by using dedicated bioinformatics tools. We thus extended the evaluation of the predictive power of these four newly developed computational approaches by a comparative study of the scores obtained by these approaches with experimental data for a total of about 1200 exonic variations. Our findings have demonstrated the reliability of these approaches, used alone or in combination, and allow to offer recommendations for their use as a filtration tool to prioritize the variations to be analysed as a priority in splicing-dedicated functional assays. Nevertheless, an exhaustive mutational analysis targeting MLH1 exon 7, has highlighted the apparent failure of these approaches, yet validated by studies focused on BRCA2 exon 7, MAPT exon 10 and MSH2 exon 5, suggesting that these methods might not be equivalently applicable to all exons and/or genes. Indeed, we have shown that this exon has particular characteristics, i.e. remarkably strong splice sites, conferring it a total resistance to splicing regulation mutations and defeating prediction tools. These findings help to better determine the limitations of these bioinformatics tools while contributing to their improvement. In spite of these advances, the pathogenicity assessment of splicing mutations remains complicated, especially of those leading to in-frame and/or partial splicing anomalies. By using variant-induced partial BRCA2 exon 3 skipping as a model system, we showed that BRCA2 tumor suppressor function tolerates a substantial reduction in expression level, as BRCA2 allele producing as much as 70% of transcript encoding deficient protein may not necessarily confer high-risk of developing cancer. Altogether, these data have important implications in the molecular diagnosis and clinical management of patients and their relatives, with a direct benefit for hereditary cancer-suspected families and should contribute to the interpretation of VSI identified by high throughput sequencing in any other genetic disease
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Fan, Hsiu-Kuei, and 范修魁. "CONSTRUCT PREDICTION MODEL OF THE ABNORMAL DEFECT BY ANALYZING ELECTRIC CHARACTER ON ARRAY’S PROCESS." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/35562779819998266713.

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碩士
元智大學
工業工程與管理學系
96
It has highly correlation between TEG(Test Element Group) electric measure value on Array’s process and Array NG Rank what is abnormal defect on Cell’s process. The panel be judged as Array NG Rank 4 will be scrapped.It can reduce the cost waste resulted from unnecessary material be input in scrapped panel if we can predict what’s Rank in Array NG precisely.Therefore,the article’ topical subject research in construct prediction model of the abnormal defect by analyzing electric character on Array’s process.It will be divide into two parts to proceed. In first phase,by different method:Multinomial Logit model and a decision tree algorithm C5.0 to find out the better predictive rate in predicting Array NG’s Rank and induce the key factor from the inside of TEG(Test Element Group) electric measure value: Ion,Ioff,Vth, Ufe,Idl,Gm,Rgl,Rs1,Rc1,Rge1,Rge2, Rsd1 and Rsd2. In second phase,i will construct prediction model of the abnormal defect by analyzing electric character on Array’s process to understand that TEG electric measure value influence the degree of Array NG Rank’s judgement. According to analysis result in article,in first phase, the models were evaluated based on the predictive accuracy rate for test sets.The Multinomial Logit model had better predictive rate(96.17%) than the predictive rate(96.10%) of C5.0 model and the key factors included Vth,Rs1,Rge2,Rsd1 and Rsd2.In second phase,the prediction model be constructed by analyzed the correlation between TEG electric measure value and Array NG Rank can have higher ability to predict Array NG Rank judged as 4.The ability can reach to 96.15% and it can save the cost includes NT 840,600 dollars and 45 hours in work per month.
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10

Huang, Fuqun. "Software Defect Defense based on Human Error Mechanisms." Doctoral thesis, 2013. http://hdl.handle.net/10316/95899.

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Documentos apresentados no âmbito do reconhecimento de graus e diplomas estrangeiros
Software defects are the critical threat to increase life-cycle costs, delay project schedule, reduce the reliability of software systems, and even cause catastrophic disasters. Since the concept of software engineering has been proposed, people have developed many technologies to prevent the introduction of software defects. However, the effects are not optimistic. So far although tremendous resources have been devoted to software testing, defects are still the major threat to the reliability of software systems. The proactive defense against software defects can be a promising philosophy to reduce costs and improve reliability. However, conventional relevant technologies such as defect prediction and defect prevention can hardly prevent the introduction of software defects. It is the time to prompt a thorough reflection on the conventional ways: the conventional technologies trend to focus on the improvement of software process, but ignore the underlying mechanisms that cause software defects. Essentially, programs are the “expression” of human thoughts, while software defects are mainly caused by human cognitive failures. Conventional software engineering technologies are designed to control and improve the process of software production, rather than directly impact on the key factor---programmer’s cognition, thus, they can only influence software quality indirectly. Once we have failed to capture the mechanisms of software defects, we can neither predict them precisely, nor prevent them fundamentally. To address these gaps, this thesis proposes the concept of defect defense based on human error mechanisms. Logically, prediction and prevention should be interconnected, since only when an event can be predicted, it can be prevented. That is to say, prediction normally provides implications for prevention. However, due to the omission of mechanisms, the conventional defect prediction is unable to achieve sufficient accuracy at early stages of software development. Thus, conventional predictions can provide little information for defect prevention. That’s why the conventional defect prediction and prevention are completely irrelevant. In this thesis, bonded by the human error mechanisms, prediction and prevention are integrated, to defend against the introduction of software defects together. The research is first carried out by summarizing the relevant research about program design cognition, with an integrated cognition model of program design constructed. Then integrate the classical theories of human errors with the domain characteristics of programming, a base of human error modes for software defects is developed. Based on the integrated cognition model and human error modes, three approaches are proposed, designed and validated. “Conventional defect prevention (DP) based on the structural taxonomy of root causes” is an improved defect prevention approach in the framework of conventional DP. Conventional DP framework is effective in preventing defects due to process problems. However, it is strongly depended on experts’ experiences and brain storms, which have limit its applications in small companies. Even for companies at high process maturity levels, it is hard to replicate the benefits of conventional DP. A structural taxonomy of root causes is proposed and validated, and the core knowledge required for root cause analysis is solidified in the knowledge base. An application case has been carried out, results show that with the assistance of the taxonomy and knowledge base, the small company at the CMM initial level can implement conventional DP effectively. “Defect Prevention by Improving Software Developers’ Meta-cognitive Ability to Prevent Human Errors” (HEDP) is an approach in the framework that is completely different from conventional DP. This approach is proposed for the reason that, individual cognitive failures are the main cause of software defects, but conventional DP has little power in affecting individual’s cognitive performances. HEDP aims to prevent defects by improving programmers’ awareness and regulation abilities under error-prone situations. HEDP is designed in the framework of meta-cognition, including two stages. The first stage concerns meta-cognitive training on human error knowledge and the second stage aims to build programmers’ experience in meta-cognitive regulation. The knowledge training consists of knowledge about program designing cognition, human error mechanisms, and error prevention strategies. The meta-cognitive regulation experience is built by the reflection in the course of problem solving and self-reviews after the defects are detected. Two application cases are studied, with the self-assessment and defect data collected. Both kinds of results show that, HEDP is effective in improving programmers’ meta-cognitive ability to prevent software defects. Furthermore, HEDP is independent of process maturity, that is to say, all organizations can implement HEDP, no matter at CMM level 5 or level 1. Most important of all, HEDP can be used to guide any programmer pursuing self-improvement in human error prevention, no matter experts or novices. “Software defect prediction based on human error mechanisms”(HEFP) is an new approach to predict the location and format of defects at the early phases of software development, i.e. phases of requirement analysis and design. Such prediction is implemented by human error scenario analysis. A controlled experiment has been designed to validate HEFP and provides empirical evidences for relevant concepts. The results show that, HEFP has predominant advantages in predicting coincident defects. HEFP has precisely predicted the location and format of 88.9% coincident defects, which are committed by 96.5% of the subjects who has committed coincident defects. Meanwhile, what the HEFP predicts are the defects at high risk. Though the number of defects predicted by HEFP only constitutes 30.8% of the total defects, but they are committed by 78.6% subjects who commit any error. In comparison, conventional predictors based on program metrics can only account for 26.8% variance of the total defects, and they can not output the accurate locations and formats of the defects. Results show that HEFP performs much better than prediction models based on program metrics, both in the accuracy and efficiency. Most important of all, HEFP can perform at the early phases of the software development, thus it can provide implications for defect prevention. In summary, the two sets of basic theories and three approaches works together, constituting the comprehensive system to defend against software defects.
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Книги з теми "Defect prediction model"

1

K, De Groh Kim, and NASA Glenn Research Center, eds. The dependence of atomic oxygen undercutting of protected polyimide Kapton® H upon defect size. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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2

Snyder, Aaron. The dependence of atomic oxygen undercutting of protected polyimide Kapton® H upon defect size. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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An investigation of gear mesh failure prediction techniques. [Washington, D.C.]: NASA, 1989.

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4

Schröder, Michael, and Axel Schwanebeck, eds. Big Data - In den Fängen der Datenkraken. Nomos Verlagsgesellschaft mbH & Co. KG, 2019. http://dx.doi.org/10.5771/9783748904373.

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Our beautiful, new digital world has a come at a price, which we are paying by relinquishing our personal data–while we are shopping, driving our cars, and chatting and surfing on the Internet. However, the intelligent algorithms needed to process this data pose a threat to freedom in our society. They analyse and evaluate us, while predicting our behaviour. Big data and data mining are the business models of the future. What does all this mean for politics, the economy, journalism and political communication? Do we have to defend basic human rights and human dignity against the digital revolution? Do we need new laws and a code of ethics for algorithms? And how will politics, the media and democracy function under these new conditions? In this book, experts from a variety of academic fields, journalism and politics discuss these questions in terms of the future and society. With contributions by Johanna Haberer, Yvonne Hofstetter, Sabine Leutheusser-Schnarrenberger, Klaus Mainzer, Daniel Moßbrucker, Peter Schaar, Michael Schröder, Axel Schwanebeck and Thomas Zeilinger.
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Частини книг з теми "Defect prediction model"

1

Mauša, Goran, and Tihana Galinac Grbac. "The Stability of Threshold Values for Software Metrics in Software Defect Prediction." In Model and Data Engineering, 81–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66854-3_7.

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2

Cui, Mengtian, Yameng Huang, and Jing Luo. "Software Defect Prediction Model Based on GA-BP Algorithm." In Cyberspace Safety and Security, 151–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37352-8_13.

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3

Zhu, Yu, Dongjin Yin, Yingtao Gan, Lanlan Rui, and Guoxin Xia. "Software Defect Prediction Model Based on Stacked Denoising Auto-Encoder." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 18–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22971-9_2.

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4

Goel, Kavya, Sonam Gupta, and Lipika Goel. "Empirical Evaluation of Local Model for Just in Time Defect Prediction." In Communication, Software and Networks, 299–310. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4990-6_26.

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5

Liu, Guang-jie, and Wen-yong Wang. "Research on an Educational Software Defect Prediction Model Based on SVM." In Entertainment for Education. Digital Techniques and Systems, 215–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14533-9_22.

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6

Awotunde, Joseph Bamidele, Sanjay Misra, Abidemi Emmanuel Adeniyi, Moses Kazeem Abiodun, Manju Kaushik, and Morolake Oladayo Lawrence. "A Feature Selection-Based K-NN Model for Fast Software Defect Prediction." In Computational Science and Its Applications – ICCSA 2022 Workshops, 49–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10542-5_4.

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7

Gautam, Abhishek, Anant Gupta, Bharti Singh, Ashwajit Singh, and Shweta Meena. "Development of Homogenous Cross-Project Defect Prediction Model Using Artificial Neural Network." In Advancements in Interdisciplinary Research, 201–12. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23724-9_19.

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Sinha, Anurag, Shubham Singh, and Devansh Kashyap. "Implication of Soft Computing and Machine Learning Method for Software Quality, Defect and Model Prediction." In Multi-Criteria Decision Models in Software Reliability, 45–80. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780367816414-3.

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Nanditha, J., K. N. Sruthi, Sreeja Ashok, and M. V. Judy. "Optimized Defect Prediction Model Using Statistical Process Control and Correlation-Based Feature Selection Method." In Advances in Intelligent Systems and Computing, 355–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23036-8_31.

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Yadav, Harikesh Bahadur, and Dilip Kumar Yadav. "A Multistage Model for Defect Prediction of Software Development Life Cycle Using Fuzzy Logic." In Advances in Intelligent Systems and Computing, 661–71. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1768-8_58.

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Тези доповідей конференцій з теми "Defect prediction model"

1

Vladu, Ana Maria, Sergiu Stelian Iliescu, and Ioana Fagarasan. "Product defect prediction model." In 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2011. http://dx.doi.org/10.1109/saci.2011.5873055.

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Huang, Song, Yaning Wu, Haijin Ji, and Chengzu Bai. "A Three-Stage Defect Prediction Model for Cross-Project Defect Prediction." In 2017 International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2017. http://dx.doi.org/10.1109/dsa.2017.39.

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Wang, Tao, and Wei-hua Li. "Naive Bayes Software Defect Prediction Model." In 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677057.

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Ba, Jie, and Shujian Wu. "SdDirM: A dynamic defect prediction model." In 2012 IEEE/ASME 8th International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE, 2012. http://dx.doi.org/10.1109/mesa.2012.6275570.

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Zhang, Feng, Audris Mockus, Iman Keivanloo, and Ying Zou. "Towards building a universal defect prediction model." In the 11th Working Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2597073.2597078.

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Alsaraireh, Jameel, and Mary Agoyi. "New Dataset for Software Defect Prediction Model." In 2022 10th International Conference on Smart Grid (icSmartGrid). IEEE, 2022. http://dx.doi.org/10.1109/icsmartgrid55722.2022.9848620.

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Humphreys, Jack, and Hoa Khanh Dam. "An Explainable Deep Model for Defect Prediction." In 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). IEEE, 2019. http://dx.doi.org/10.1109/raise.2019.00016.

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8

Yang, Weimin, and Longshu Li. "A Rough Set Model for Software Defect Prediction." In 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2008. http://dx.doi.org/10.1109/icicta.2008.76.

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Zheng, Wei, Lijuan Tan, and Chengbin Liu. "Software Defect Prediction Method Based on Transformer Model." In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9498179.

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Zhou, Yan, Chun Shan, Shiyou Sun, Shengjun Wei, and Sicong Zhang. "Software Defect Prediction Model Based On KPCA-SVM." In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2019. http://dx.doi.org/10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00244.

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Звіти організацій з теми "Defect prediction model"

1

Dudley, Lynn M., Uri Shani, and Moshe Shenker. Modeling Plant Response to Deficit Irrigation with Saline Water: Separating the Effects of Water and Salt Stress in the Root Uptake Function. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7586468.bard.

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Standard salinity management theory, derived from blending thermodynamic and semi- empirical considerations leads to an erroneous perception regarding compensative interaction among salinity stress factors. The current approach treats matric and osmotic components of soil water potential separately and then combines their effects to compute overall response. With deficit water a severe yield decrease is expected under high salinity, yet little or no reduction is predicted for excess irrigation, irrespective of salinity level. Similarly, considerations of competition between chloride and nitrate ions have lead to compensation hypothesis and to application of excess nitrate under saline conditions. The premise of compensative interaction of growth factors behind present practices (that an increase in water application alleviates salinity stress) may result in collateral environmental damage. Over-irrigation resulting in salinization and elevated ground water threatens productivity on a global scale. Other repercussions include excessive application of nitrate to compensate for salinity, unwillingness to practice deficit irrigation with saline water, and under-utilization of marginal water. The objectives for the project were as follows: 1) To develop a database for model parameterization and validation by studying yield and transpiration response to water availability, excessive salinity and salt composition. 2) To modify the root sink terms of an existing mechanism-based model(s) of water flow, transpiration, crop yield, salt transport, and salt chemistry. 3) To develop conceptual and quantitative models of ion uptake that considers the soil solution concentration and composition. 4) To develop a conceptual and quantitative models of effects of NaCl and boron accumulation on yield and transpiration. 5) To add a user interface to the water flow, transpiration, crop yield, salt transport, chemistry model to make it easy for others to use. We conducted experiments in field plots and lysimeters to study biomass production and transpiration of com (Zeamays cv. Jubilee), melon (Cucumismelo subsp. melo cv. Galia), tomato (Lycopersiconesculentum Mill. cv. 5656), onion (Alliumcepa L. cv. HA 944), and date palms (Phoenix Dactylifera L. cv. Medjool) under salinity combined with water or with nitrate (growth promoters) or with boron (growth inhibitor). All factors ranged from levels not limiting to plant function to severe inhibition. For cases of combined salinity with water stress, or excess boron, we observed neither additive nor compensative effects on plant yield and transpiration. In fact, yield and transpiration at each combination of the various factors were primarily controlled by one of them, the most limiting factor to plant activity. We proposed a crop production model of the form Yr = min{gi(xi), where Yr = Yi ym-1 is relative yield,Ym is the maximum yield obtained in each experiment, Xi is an environmental factor, gi is a piecewise-linear response function, Yi is yield of a particular treatment. We selected a piecewise-linear approach because it highlights the irrigation level where the response to one factor ceases and a second factor begins. The production functions generate response "envelopes" containing possible yields with diagonal lines represent response to Xi alone and the lines parallel to the X-axis represent response to salinity alone. A multiplicative model was also derived approximating the limiting behaviour for incorporation in a hydrochemical model. The multiplicative model was selected because the response function was required to be continuous. The hydrochemical model was a better predictor of field-measured water content and salt profiles than models based on an additive and compensative model of crop response to salinity and water stress.
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