Готові списки джерел за темами / Software defect

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Software defect"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 27 липня 2025

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Статті в журналах з теми "Software defect"

1

Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Анотація:
Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to
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2

Kumaresh, Sakthi, and R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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Анотація:
“Quality is never an accident; it is always the result of intelligent effort” [10]. In the process of making quality software product, it is necessary to have effective defect prevention process, which will minimize the risk of making defects /errors in software deliverables. An ideal approach would involve effective software development process with an integrated defect prevention process. This paper presents a Defect Prevention Model in which Defect Prevention Process(DPP) is integrated into software development life cycle to reduce the defects at early stages itself, thereby reducing th
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3

Malhotra, Ruchika, and Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques." International Journal of Software Engineering and Knowledge Engineering 31, no. 02 (2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

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Анотація:
Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build de
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4

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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Анотація:
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 wit
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5

Sakthi, Kumaresh, and Ramachandran Baskaran. "Defect Prevention Based on 5 Dimensions of Defect Origin." International Journal of Software Engineering & Applications (IJSEA) 3, no. 4 (2020): 87–98. https://doi.org/10.5281/zenodo.4047989.

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Анотація:
“Discovering the unexpected is more important than confirming the known [7]. In software development, the “unexpected” one relates to defects. These defects when unattended would cause failure to the product and risk to the users. The increasing dependency of society on software and the crucial consequences that a failure can cause requires the need to find out the defects at the origin itself. Based on the lessons learnt from the earlier set of projects, a defect framework highlighting the 5 Dimensions (Ds) of defect origin is proposed in this work. The defect framework is b
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6

Diwan, Sinan, and Abdul Syukor Mohamad. "Machine Learning Empowered Software Prediction System." Wasit Journal of Computer and Mathematics Science 1, no. 3 (2022): 54–64. http://dx.doi.org/10.31185/wjcm.61.

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Анотація:
Prediction of software defects is one of the most active study fields in software engineering today. Using a defect prediction model, a list of code prone to defects may be compiled. Using a defect prediction model, software may be made more reliable by identifying and discovering faults before or during the software enhancement process. Defect prediction will play an increasingly important role in the design process as the scope of software projects grows. Bugs or the number of bugs used to measure the performance of a defect prediction procedure are referred to as "bugs" in this context. Def
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7

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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8

Simatupang, Johannes. "Software Defect Elimination Information System in Software House Company." Jurnal Indonesia Sosial Teknologi 5, no. 8 (2024): 2936–45. http://dx.doi.org/10.59141/jist.v5i8.1289.

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Анотація:
In software that is free from defects or errors, a fixed method and technique of software testing is required. For this step, an Information System is needed to help eliminate software defects, so that testing or testing work is not a burden on software costs but is a perfection of software development work so that Zero Defect Application Software can be realized. From several software testing methods and techniques, a strategy suitable for the size of a software development project is required. So even though the software development project is small, it still requires a suitable test. And fo
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9

WANG, Qing. "Software Defect Prediction." Journal of Software 19, no. 7 (2008): 1565–80. http://dx.doi.org/10.3724/sp.j.1001.2008.01565.

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10

Malhotra, Ruchika, and Madhukar Cherukuri. "Convolutional Neural Networks for Software Defect Categorization: An Empirical Validation." JUCS - Journal of Universal Computer Science 31, no. (1) (2025): 22–51. https://doi.org/10.3897/jucs.117185.

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Анотація:
The escalating complexity and scale of software systems have rendered them increasingly susceptible to a variety of defects. To empower maintenance teams to efficiently prioritize and resolve defects, Software Defect Categorization (SDC) models have emerged, offering the classification of software defects into categories such as "high," "medium," or "low." This study embarks on the development of SDC models, based on three critical defect attributes: i) the maintenance effort required to rectify a defect, ii) the change impact on the software induced by defect resolution, and iii) a combined a
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Дисертації з теми "Software defect"

1

Ye, Xin. "Automated Software Defect Localization." Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1462374079.

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2

Jain, Achin. "Software defect content estimation: A Bayesian approach." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/26932.

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Анотація:
Software inspection is a method to detect errors in software artefacts early in the development cycle. At the end of the inspection process the inspectors need to make a decision whether the inspected artefact is of sufficient quality or not. Several methods have been proposed to assist in making this decision like capture recapture methods and Bayesian approach. In this study these methods have been analyzed and compared and a new Bayesian approach for software inspection is proposed. All of the estimation models rely on an underlying assumption that the inspectors are independent. However, t
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3

Hassan, Syed Karimuddin and Syed Muhammad. "Defect Detection in SRS using Requirement Defect Taxonomy." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5253.

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Анотація:
Context: Defects occurred in the SRS may cause problems in project due to implementation of poor requirements which require extra time, effort, resources and budget to complete it. Reading techniques i.e., checklist based reading (CBR) helps to guide reviewers in identifying defects in software requirement specification (SRS) during individual requirement inspections. Checklists contain potential defects/problems to look for, but often lack clear definitions with examples of the problem, and also their abstractions are different. Therefore, there is a need for identifying existing defects and
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4

Porto, Faimison Rodrigues. "Cross-project defect prediction with meta-Learning." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21032018-163840/.

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Анотація:
Defect prediction models assist tester practitioners on prioritizing the most defect-prone parts of the software. The approach called Cross-Project Defect Prediction (CPDP) refers to the use of known external projects to compose the training set. This approach is useful when the amount of historical defect data of a company to compose the training set is inappropriate or insufficient. Although the principle is attractive, the predictive performance is a limiting factor. In recent years, several methods were proposed aiming at improving the predictive performance of CPDP models. However, to the
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5

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 f
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Sherwood, Patricia Ann. "Inspections : software development process for building defect free software applied in a small-scale software development environment /." Online version of thesis, 1990. http://hdl.handle.net/1850/10598.

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7

Hameed, Muhammad Muzaffar, and Muhammad Zeeshan ul Haq. "DefectoFix : An interactive defect fix logging tool." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5268.

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Анотація:
Despite the large efforts made during the development phase to produce fault free system, most of the software implementations still require the testing of entire system. The main problem in the software testing is the automation that could verify the system without manual intervention. Recent work in software testing is related to the automated fault injection by using fault models from repository. This requires a lot of efforts, which adds to the complexity of the system. To solve this issue, this thesis suggests DefectoFix framework. DefectoFix is an interactive defect fix logging tools tha
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8

Phaphoom, Nattakarn. "Pair Programming and Software Defects : A Case Study." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3513.

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Анотація:
Pair programming is a programming technique in which two programmers sit literally side by side working on the same task at the same computer. One member of a pair called “driver” is in charge of writing the code. The other member plays a role of “navigator”, working on the more strategic tasks, such as looking for tactical error, thinking about overall structure, and finding better alternatives. Pair programming is claimed to improve product quality, reduce defects, and shorten time to market. On the other hand, it has been criticized on cost efficiency. To increase a body of evidence regardi
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9

Akinwale, Olusegun. "DuoTracker tool support for software defect data collection and analysis /." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447633.

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Gray, David Philip Harry. "Software defect prediction using static code metrics : formulating a methodology." Thesis, University of Hertfordshire, 2013. http://hdl.handle.net/2299/11067.

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Анотація:
Software defect prediction is motivated by the huge costs incurred as a result of software failures. In an effort to reduce these costs, researchers have been utilising software metrics to try and build predictive models capable of locating the most defect-prone parts of a system. These areas can then be subject to some form of further analysis, such as a manual code review. It is hoped that such defect predictors will enable software to be produced more cost effectively, and/or be of higher quality. In this dissertation I identify many data quality and methodological issues in previous defect
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Книги з теми "Software defect"

1

Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2.

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Cai, Kai-Yuan. Software Defect and Operational Profile Modeling. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5593-3.

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3

Younessi, Houman. Object-oriented defect management of software. Prentice Hall PTR, 2002.

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4

Miller, Ann K. Engineering quality software: Defect detection and prevention. Addison-Wesley, 1992.

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5

Peterson, Ivars. Fatal Defect: Chasing Killer Computer Bugs. Times Books, 1995.

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6

Peterson, Ivars. Fatal Defect: Chasing Killer Computer Bugs. Vantage Books, 1996.

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7

Huizinga, Dorota. Automated defect prevention: Best practices in software management. Wiley, 2007.

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8

Ferdinand, Arthur E. Systems, software, and qualityengineering: Applying defect behavior theory to programming. Van Nostrand Reinhold, 1993.

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9

Great Britain. Ministry of Defence. Directorate of Standardization. The procurement of safety critical software in defence equipment. MOD, 1991.

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10

Great Britain. Ministry of Defence. Directorate of Standardization. The procurement of safety related software in defence equipment. MoD, 1995.

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Частини книг з теми "Software defect"

1

Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "Heterogeneous Defect Prediction." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_5.

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Kumar, Sushil, Meera Sharma, S. K. Muttoo, and V. B. Singh. "Autoclassify Software Defects Using Orthogonal Defect Classification." In Computational Science and Its Applications – ICCSA 2022 Workshops. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10548-7_23.

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Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "Cross-Project Defect Prediction." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_4.

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Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "Within-Project Defect Prediction." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_3.

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Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "Introduction." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_1.

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Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "Machine Learning Techniques for Intelligent SDP." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_2.

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Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "Conclusion." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_8.

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Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "An Empirical Study on HDP Approaches." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_6.

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Jing, Xiao-Yuan, Haowen Chen, and Baowen Xu. "Other Research Questions of SDP." In Intelligent Software Defect Prediction. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2842-2_7.

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Rodríguez, Daniel, R. Ruiz, J. C. Riquelme, and Rachel Harrison. "Subgroup Discovery for Defect Prediction." In Search Based Software Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23716-4_25.

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Тези доповідей конференцій з теми "Software defect"

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Xu, Fanjiang, and Zeyu Sun. "Defect-Introducing Defect Prediction Testing." In 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). IEEE, 2024. http://dx.doi.org/10.1109/qrs-c63300.2024.00057.

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Wicaksono, Pandu, Panji Arisaputra, Rezky Yunanda, Willy Kristian, and Almuzhidul Mujhid. "Software Defect Prediction Using Ensemble Technique." In 2024 International Conference on Information Management and Technology (ICIMTech). IEEE, 2024. https://doi.org/10.1109/icimtech63123.2024.10780854.

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Zheng, Liyuan, Yang Xie, Boyuan Hou, and Xin Xie. "Software Defect Classification and Analysis Study." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667506.

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Gita Gayatri, Nyoman Ayu, Haryono Soeparno, Ford Lumban Gaol, and Yulyani Arifin. "Enhancing Software Quality Through Defect Prediction." In 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024. http://dx.doi.org/10.1109/iccit62134.2024.10701182.

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Mao, Xuan, Zijie Huang, Jianxin Ge, Chao Wang, Wuxu Wang, and Lizhi Cai. "Towards Defect Prediction for Quantum Software*." In 2025 IEEE/ACM International Workshop on Quantum Software Engineering (Q-SE). IEEE, 2025. https://doi.org/10.1109/q-se66736.2025.00007.

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Jadhav, Shivani, Saranya Manikandan, and Duksan Ryu. "Enhancing the Software Defect Prediction using AutoInt." In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2025. https://doi.org/10.1109/bigcomp64353.2025.00029.

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Suhag, Vikas, Sanjay Kumar Dubey, and Bhupendra Kumar Sharma. "A Multi-model Software Defect Recommendation System." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10828979.

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Benson, Markland J. "Toward Intelligent Software Defect Detection - Learning Software Defects by Example." In 2011 34th Annual IEEE Software Engineering Workshop (SEW). IEEE, 2011. http://dx.doi.org/10.1109/sew.2011.26.

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Gokhale, Swapna S., and Robert Mullen. "Software defect repair times." In the 4th international workshop. ACM Press, 2008. http://dx.doi.org/10.1145/1370788.1370810.

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Yusop, Nor Shahida Mohamad. "Understanding Usability Defect Reporting in Software Defect Repositories." In ASWEC ' 15 Vol. II: ASWEC 2015 24th Australasian Software Engineering Conference. ACM, 2015. http://dx.doi.org/10.1145/2811681.2817757.

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Звіти організацій з теми "Software defect"

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Thomas, R. Edward. Hardwood log defect photographic database, software and user's guide. U.S. Department of Agriculture, Forest Service, Northern Research Station, 2009. http://dx.doi.org/10.2737/nrs-gtr-40.

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Kiefner and Leewis. L52314 Pipeline Defect Assessment - A Review and Comparison of Commonly Used Methods. Pipeline Research Council International, Inc. (PRCI), 2010. http://dx.doi.org/10.55274/r0010440.

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Анотація:
There is a need in the industry in assessing the severity of a particular anomaly (dent, gouge, corrosion pit, etc.) in a pipe as to whether or not repair or removal is warranted. Along with the determination of repair there is also a need for prioritizing in-line-inspection (ILI) data, and for making remaining-life assessments of said pipe. This document catalogs analysis methods and computational tools for addressing defects and damage discovered on in-service pipelines. It includes example calculations where closed-form algorithms are available in the public domain. It lists and describes s
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Nieland and Ying. L52105 Improvement in Performance in the Mark III Elastic Wave. Pipeline Research Council International, Inc. (PRCI), 2003. http://dx.doi.org/10.55274/r0011087.

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Анотація:
The Elastic Wave (EW) technology has a proven record of detecting longitudinal cracking in gas and liquid pipelines. However, the historical dig success rate for this inspection tool is only 1 in 4; i.e. for every four excavations carried out by pipeline operators, following an EW inspection, typically a crack or crack field will only be found at one of those excavations. This project set out to: Understand the ultrasonic data available to the inspection tool, establish whether there is information in the raw signal to differentiate between cracks and non-cracks, develop potential crack discri
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4

Bruce and Fiore. L51629 Users Manual-Field Validation of the Low-Frequency Eddy Current Instrument-Software Listings. Pipeline Research Council International, Inc. (PRCI), 1990. http://dx.doi.org/10.55274/r0010602.

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Анотація:
When an eddy current probe is placed near a conductive material, the alternating magnetic field from the probe causes electrical currents to flow in the material. These currents have associated with them their own magnetic field, which opposes the original magnetic field from the coil. The result is that the impedance of the probe is greatly reduced by the presence of the conducting material. If the conductor is also magnetic, as is normal steel, the situation is similar though slightly more complicated. Here, the impedance of the probe may be either increased or decreased depending on the per
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5

Snijders, J., C. Morrow, and R. van Mook. Software Defects Considered Harmful. RFC Editor, 2022. http://dx.doi.org/10.17487/rfc9225.

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6

Krivoi, Kallmeyer, and Baranyak. L52199 Nopig Metal-Loss Detection System for Non-Piggable-Pipelines. Pipeline Research Council International, Inc. (PRCI), 2005. http://dx.doi.org/10.55274/r0011343.

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Анотація:
This project investigated and upgraded the digital filtering used to interpret NoPig displacement data. This upgrade should allow the system to be used on long seam welded pipes. Limited laboratory testing was performed to show the improvement on ERW pipe. The NoPig method is a non-destructive testing method for unpiggable pipelines which uses above ground measurements for detecting and sizing wall thickness anomalies like corrosion. The method uses an applied current containing several distinct frequencies between two points on a pipeline up to 1 km apart. The resultant magnetic field is meas
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7

Zhang. L52052 Control of Horizontal Beam Width with Phased Array Transducers. Pipeline Research Council International, Inc. (PRCI), 2008. http://dx.doi.org/10.55274/r0010945.

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Анотація:
Accurate defect sizing is becoming more and more critical in pipeline welds due to the application of Engineering Critical Assessment, demanding deep sea applications, the arrival of high performance piping, and increased public safety demands. This project improved horizontal beam focusing for automated ultrasonic testing; curved arrays, focused lenses and electronic focusing using phased arrays were investigated. Two target applications were selected: thickwalled risers and tendons, and thinner walled high performance pipes for onshore. Extensive computer modeling was performed to optimize t
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8

Leis, Brian. L51794A Failure Criterion for Residual Strength of Corrosion Defects in Moderate to High Toughness Pipe. Pipeline Research Council International, Inc. (PRCI), 2000. http://dx.doi.org/10.55274/r0011253.

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Анотація:
This project extends the investigation of the remaining strength of blunt and sharp flaws in pipe to develop a new, simple equation, known as PCORRC, for predicting the remaining strength of corrosion defects in moderate- to high-toughness steels that fail by the mechanism of plastic collapse. This report summarizes the development of this criterion, which began with the enhancement of a special-purpose, analytical, finite-element-based software model (PCORR) for analyzing complex loadings on corrosion and other blunt defects. The analytical tool was then used to compare the influence of diffe
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9

Florac, William A. Software Quality Measurement: A Framework for Counting Problems and Defects. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada258556.

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10

Hendrick, Stephen, and Martin Mckeay. Addressing Cybersecurity Challenges in Open Source Software. The Linux Foundation, 2022. https://doi.org/10.70828/jxey9389.

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Анотація:
While open source software is ubiquitous and generally regarded as being secure, software development practices vary widely across projects regarding application development practices, protocols to respond to defects, or lack of standardized selection criteria to determine which software components are more likely to be secure. Consequently, software supply chains are vulnerable to attack, with implications and challenges for open source project communities. To help improve the state of software supply chain security, new research was conducted in partnership with the Open Source Security Foun
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