Literatura académica sobre el tema "Defect prediction model"
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Artículos de revistas sobre el tema "Defect prediction model"
Et.al, Christopher Paulraj. "An intelligent Model for Defect Prediction in Spot Welding". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, n.º 3 (11 de abril de 2021): 3991–4002. http://dx.doi.org/10.17762/turcomat.v12i3.1689.
Texto completoMemon, Mashooque Ahmed, Mujeeb-ur-Rehman Maree Baloch, Muniba Memon y Syed Hyder Abbas Musavi. "A Regression Analysis Based Model for Defect Learning and Prediction in Software Development". July 2021 40, n.º 3 (1 de julio de 2021): 617–29. http://dx.doi.org/10.22581/muet1982.2103.15.
Texto completoYuan, Yuyu, Chenlong Li y Jincui Yang. "An Improved Confounding Effect Model for Software Defect Prediction". Applied Sciences 13, n.º 6 (8 de marzo de 2023): 3459. http://dx.doi.org/10.3390/app13063459.
Texto completoZhang, Wei, Zhen Yu Ma, Qing Ling Lu, Xiao Bing Nie y Juan Liu. "Research on Software Defect Prediction Method Based on Machine Learning". Applied Mechanics and Materials 687-691 (noviembre de 2014): 2182–85. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2182.
Texto completoFalessi, Davide, Aalok Ahluwalia y 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, n.º 1 (31 de enero de 2022): 1–26. http://dx.doi.org/10.1145/3467895.
Texto completoCHANG, CHING-PAO. "INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION". International Journal of Software Engineering and Knowledge Engineering 23, n.º 02 (marzo de 2013): 147–72. http://dx.doi.org/10.1142/s0218194013500022.
Texto completoNevendra, Meetesh y Pradeep Singh. "Cross-Project Defect Prediction with Metrics Selection and Balancing Approach". Applied Computer Systems 27, n.º 2 (1 de diciembre de 2022): 137–48. http://dx.doi.org/10.2478/acss-2022-0015.
Texto completoZhang, Jie, Gang Wang, Haobo Jiang, Fangzheng Zhao y 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.
Texto completoPeng, Xuemei. "Research on Software Defect Prediction and Analysis Based on Machine Learning". Journal of Physics: Conference Series 2173, n.º 1 (1 de enero de 2022): 012043. http://dx.doi.org/10.1088/1742-6596/2173/1/012043.
Texto completoHan, Wan Jiang, He Yang Jiang, Yi Sun y Tian Bo Lu. "Software Defect Distribution Prediction for BOSS System". Applied Mechanics and Materials 701-702 (diciembre de 2014): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.67.
Texto completoTesis sobre el tema "Defect prediction model"
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.
Texto completoBowes, David Hutchinson. "Factors affecting the performance of trainable models for software defect prediction". Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/10978.
Texto completoHagene, Matthew Ray. "Momentum Defect Superposition Model for Predicting Depth-Averaged Velocities in Trapezoidal Channels". OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/553.
Texto completoJames, 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.
Texto completoRaiker, 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.
Texto completoID: 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
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/.
Texto completoThe 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.
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.
Texto completoTubeuf, 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.
Texto completoThe 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
Fan, Hsiu-Kuei y 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.
Texto completo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.
Huang, Fuqun. "Software Defect Defense based on Human Error Mechanisms". Doctoral thesis, 2013. http://hdl.handle.net/10316/95899.
Texto completoSoftware 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.
Libros sobre el tema "Defect prediction model"
K, De Groh Kim y 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.
Buscar texto completoSnyder, 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.
Buscar texto completoAn investigation of gear mesh failure prediction techniques. [Washington, D.C.]: NASA, 1989.
Buscar texto completoSchröder, Michael y Axel Schwanebeck, eds. Big Data - In den Fängen der Datenkraken. Nomos Verlagsgesellschaft mbH & Co. KG, 2019. http://dx.doi.org/10.5771/9783748904373.
Texto completoCapítulos de libros sobre el tema "Defect prediction model"
Mauša, Goran y Tihana Galinac Grbac. "The Stability of Threshold Values for Software Metrics in Software Defect Prediction". En Model and Data Engineering, 81–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66854-3_7.
Texto completoCui, Mengtian, Yameng Huang y Jing Luo. "Software Defect Prediction Model Based on GA-BP Algorithm". En Cyberspace Safety and Security, 151–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37352-8_13.
Texto completoZhu, Yu, Dongjin Yin, Yingtao Gan, Lanlan Rui y Guoxin Xia. "Software Defect Prediction Model Based on Stacked Denoising Auto-Encoder". En 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.
Texto completoGoel, Kavya, Sonam Gupta y Lipika Goel. "Empirical Evaluation of Local Model for Just in Time Defect Prediction". En Communication, Software and Networks, 299–310. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4990-6_26.
Texto completoLiu, Guang-jie y Wen-yong Wang. "Research on an Educational Software Defect Prediction Model Based on SVM". En 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.
Texto completoAwotunde, Joseph Bamidele, Sanjay Misra, Abidemi Emmanuel Adeniyi, Moses Kazeem Abiodun, Manju Kaushik y Morolake Oladayo Lawrence. "A Feature Selection-Based K-NN Model for Fast Software Defect Prediction". En 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.
Texto completoGautam, Abhishek, Anant Gupta, Bharti Singh, Ashwajit Singh y Shweta Meena. "Development of Homogenous Cross-Project Defect Prediction Model Using Artificial Neural Network". En Advancements in Interdisciplinary Research, 201–12. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23724-9_19.
Texto completoSinha, Anurag, Shubham Singh y Devansh Kashyap. "Implication of Soft Computing and Machine Learning Method for Software Quality, Defect and Model Prediction". En Multi-Criteria Decision Models in Software Reliability, 45–80. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780367816414-3.
Texto completoNanditha, J., K. N. Sruthi, Sreeja Ashok y M. V. Judy. "Optimized Defect Prediction Model Using Statistical Process Control and Correlation-Based Feature Selection Method". En 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.
Texto completoYadav, Harikesh Bahadur y Dilip Kumar Yadav. "A Multistage Model for Defect Prediction of Software Development Life Cycle Using Fuzzy Logic". En 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.
Texto completoActas de conferencias sobre el tema "Defect prediction model"
Vladu, Ana Maria, Sergiu Stelian Iliescu y Ioana Fagarasan. "Product defect prediction model". En 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2011. http://dx.doi.org/10.1109/saci.2011.5873055.
Texto completoHuang, Song, Yaning Wu, Haijin Ji y Chengzu Bai. "A Three-Stage Defect Prediction Model for Cross-Project Defect Prediction". En 2017 International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2017. http://dx.doi.org/10.1109/dsa.2017.39.
Texto completoWang, Tao y Wei-hua Li. "Naive Bayes Software Defect Prediction Model". En 2010 International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE, 2010. http://dx.doi.org/10.1109/cise.2010.5677057.
Texto completoBa, Jie y Shujian Wu. "SdDirM: A dynamic defect prediction model". En 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.
Texto completoZhang, Feng, Audris Mockus, Iman Keivanloo y Ying Zou. "Towards building a universal defect prediction model". En the 11th Working Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2597073.2597078.
Texto completoAlsaraireh, Jameel y Mary Agoyi. "New Dataset for Software Defect Prediction Model". En 2022 10th International Conference on Smart Grid (icSmartGrid). IEEE, 2022. http://dx.doi.org/10.1109/icsmartgrid55722.2022.9848620.
Texto completoHumphreys, Jack y Hoa Khanh Dam. "An Explainable Deep Model for Defect Prediction". En 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.
Texto completoYang, Weimin y Longshu Li. "A Rough Set Model for Software Defect Prediction". En 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2008. http://dx.doi.org/10.1109/icicta.2008.76.
Texto completoZheng, Wei, Lijuan Tan y Chengbin Liu. "Software Defect Prediction Method Based on Transformer Model". En 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9498179.
Texto completoZhou, Yan, Chun Shan, Shiyou Sun, Shengjun Wei y Sicong Zhang. "Software Defect Prediction Model Based On KPCA-SVM". En 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.
Texto completoInformes sobre el tema "Defect prediction model"
Dudley, Lynn M., Uri Shani y 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, marzo de 2003. http://dx.doi.org/10.32747/2003.7586468.bard.
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