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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "SOFTWARE DEFECT REPORTS"

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Jindal, Rajni, Ruchika Malhotra, and Abha Jain. "Predicting Software Maintenance Effort by Mining Software Project Reports Using Inter-Version Validation." International Journal of Reliability, Quality and Safety Engineering 23, no. 06 (December 2016): 1640009. http://dx.doi.org/10.1142/s021853931640009x.

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Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description. Defect description describes the cause of defect which might have occurred in the previous version of the software due to which either new LOC needs to be inserted or existing LOC need to be deleted or modified. A lot of effort is required to correct the defects identified in software at the maintenance phase i.e., when software is delivered at the customers end. Thus, in this paper, we intend to predict maintenance effort by analyzing the defect reports using text mining techniques and thereafter developing the prediction models using suitable machine learning algorithms viz. Multi-Layer Perceptron (MLP), Radial-Basis Function (RBF) network and Decision Tree (DT). We have considered the changes between three successive versions of ‘MMS’ application package of Android operating system and have performed inter-version validation where the model predicted using the version ‘v’ is validated on the subsequent version i.e., ‘v+1’. The performance of the model was evaluated using Receiver Operating Characteristics (ROC) analysis. The results indicated that the model predicted on ‘MMS’ 4.0 version using MLP algorithm has shown good results when validated on ‘MMS’ 4.1 version. On the other hand, the performance of RBF and DT algorithms has been consistently average in predicting the maintenance effort.
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Malhotra, Ruchika, Nidhi Kapoor, Rishabh Jain, and Sahaj Biyani. "Severity Assessment of Software Defect Reports using Text Classification." International Journal of Computer Applications 83, no. 11 (December 18, 2013): 13–16. http://dx.doi.org/10.5120/14492-2622.

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Jindal, Rajni, Ruchika Malhotra, and Abha Jain. "Prediction of defect severity by mining software project reports." International Journal of System Assurance Engineering and Management 8, no. 2 (March 10, 2016): 334–51. http://dx.doi.org/10.1007/s13198-016-0438-y.

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Marappan, Shanmugasundaram, Archana Kollu, Ismail Keshta, Shehab Mohamed Beram, Sahil Bhende, and Karthikeyan Kaliyaperumal. "An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software." Scientific Programming 2022 (September 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/2302027.

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The resolution of a software bug depends on the severity of the defect report. Open-source software defect tracking solutions have taken over as the principal means of processing enormous amounts of defect information data due to the ongoing increase in software scale. Dealing with software faults requires analyzing the implications of defect report severity in the data warehouse. Thus, the authors have proposed an optimized systematic approach through the research and analysis of Bugzilla defect tracking system data in this study, where it is found that the attribute characteristics of different projects are quite different and the statistical features of the repair rate, resolution time, developers, components, and other attributes are consistent. This technique, therefore, assumes that a rise in the severity of software defect reports will result in a rise in the defect repair rate and that the severity is normally based on the severity distribution of various components and projects. According to the study’s findings, developers hold the most defects when the repair rate is low and the defect resolution time is shortest.
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Mellegard, Niklas, Hakan Burden, Daniel Levin, Kenneth Lind, and Ana Magazinius. "Contrasting Big Bang With Continuous Integration Through Defect Reports." IEEE Software 37, no. 3 (May 2020): 14–20. http://dx.doi.org/10.1109/ms.2018.2880822.

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Sultan, Torky, Ayman E. Khedr, and Mostafa Sayed. "A Proposed Defect Tracking Model for Classifying the Inserted Defect Reports to Enhance Software Quality Control." International Journal of Computer Applications 67, no. 14 (April 18, 2013): 1–7. http://dx.doi.org/10.5120/11460-7068.

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Sultan, Torky, Ayman Khedr, and Mostafa Sayed. "A Proposed Defect Tracking Model for Classifying the Inserted Defect Reports to Enhance Software Quality Control." Acta Informatica Medica 21, no. 2 (2013): 103. http://dx.doi.org/10.5455/aim.2013.21.103-108.

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Yadla, Suresh, Jane Huffman Hayes, and Alex Dekhtyar. "Tracing requirements to defect reports: an application of information retrieval techniques." Innovations in Systems and Software Engineering 1, no. 2 (July 29, 2005): 116–24. http://dx.doi.org/10.1007/s11334-005-0011-3.

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Pipitone, J., and S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models." Geoscientific Model Development 5, no. 4 (August 9, 2012): 1009–22. http://dx.doi.org/10.5194/gmd-5-1009-2012.

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Анотація:
Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model, one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.
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Pipitone, J., and S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models." Geoscientific Model Development Discussions 5, no. 1 (February 15, 2012): 347–82. http://dx.doi.org/10.5194/gmdd-5-347-2012.

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Анотація:
Abstract. A climate model is an executable theory of the climate; the model encapsulates climatological theories in software so that they can be simulated and their implications investigated. Thus, in order to trust a climate model one must trust that the software it is built from is built correctly. Our study explores the nature of software quality in the context of climate modelling. We performed an analysis of defect reports and defect fixes in several versions of leading global climate models by collecting defect data from bug tracking systems and version control repository comments. We found that the climate models all have very low defect densities compared to well-known, similarly sized open-source projects. We discuss the implications of our findings for the assessment of climate model software trustworthiness.
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Дисертації з теми "SOFTWARE DEFECT REPORTS"

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

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CAVALCANTI, Diego Tavares. "Estudo do uso de vocabulários para analisar o impacto de relatórios de defeitos a código-fonte." Universidade Federal de Campina Grande, 2012. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1839.

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Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-09-28T14:01:43Z No. of bitstreams: 1 DIEGO TAVARES CAVALCANTI - DISSERTAÇÃO PPGCC 2012..pdf: 11733349 bytes, checksum: 59909ce95d6ea71dea6e9686d3d20c33 (MD5)
Made available in DSpace on 2018-09-28T14:01:43Z (GMT). No. of bitstreams: 1 DIEGO TAVARES CAVALCANTI - DISSERTAÇÃO PPGCC 2012..pdf: 11733349 bytes, checksum: 59909ce95d6ea71dea6e9686d3d20c33 (MD5) Previous issue date: 2012-11-26
Localizar e corrigir defeitos são tarefas comuns no processo de manutenção de software. Entretanto, a atividade de localizar entidades de código que são possivelmente defeituosas e que necessitam ser modificadas para a correção de um defeito, não é trivial. Geralmente, desenvolvedores realizam esta tarefa por meio de um processo manual de leitura e inspeção do código, bem como de informações cadastradas em relatórios de defeitos. De fato, é necessário que os desenvolvedores tenham um bom conhecimento da arquitetura e do design do software a fim de realizarem tal tarefa. Entretanto, este conhecimento fica espalhado por entre a equipe e requer tempo para ser adquirido por novatos. Assim, é necessário o desenvolvimento de técnicas que auxiliem na tarefa de análise de impacto de relatórios de defeitos no código, independente da experiência do desenvolvedor que irá executá-la. Neste trabalho, apresentamos resultados de um estudo empírico no qual avaliamos se a análise automática de vocabulários de relatórios de defeitos e de software pode ser útil na tarefa de localizar defeitos no código. Nele, analisamos similaridade de vocabulários como fator para sugerir classes que são prováveis de serem impactadas por um dado relatório de defeito. Realizamos uma avaliação com oito projetos maduros de código aberto, desenvolvidos em Java, que utilizam Bugzilla e JIRA como seus repositórios de defeitos. Nossos resultados indicam que a análise de ambos os vocabulários é, de fato, uma fonte valiosa de informação, que pode ser utilizada para agilizar a tarefa de localização de defeitos. Para todos os sistemas estudados, ao considerarmos apenas análise de vocabulário, vimos que, mesmo com um ranking contendo apenas 8% das classes de um projeto, foi possível encontrar classes relacionadas ao defeito buscado em até 75% dos casos. Portanto, podemos concluir que, mesmo que não possamos utilizar vocabulários de software e de relatórios de defeitos como únicas fontes de informação, eles certamente podem melhorar os resultados obtidos, ao serem combinados com técnicas complementares.
Locating and fixing bugs described in bug reports are routine tasks in software development processes. A major effort must be undertaken to successfully locate the (possibly faulty) entities in the code that must be worked on. Generally, developers map bug reports to code through manual reading and inspection of both bug reports and the code itself. In practice, they must rely on their knowledge about the software architecture and design to perform the mapping in an efficient and effective way. However, it is well known that architectural and design knowledge is spread out among developers. Hence, the success of such a task is directly depending on choosing the right developer. In this paper, we present results of an empirical study we performed to evaluate whether the automated analysis of bug reports and software vocabularies can be helpful in the task of locating bugs. We conducted our study on eight versions of six mature Java open-source projects that use Bugzilla and JIRA as bug tracking systems. In our study, we have used Information Retrieval techniques to assess the similarity of bug reports and code entities vocabularies. For each bug report, we ranked ali code entities according to the measured similarity. Our results indicate that vocabularies are indeed a valuable source of information that can be used to narrow down the bug-locating task. For ali the studied systems, considering vocabulary similarity only, a Top 8% list of entities has about 75% of the target entities. We conclude that while vocabularies cannot be the sole source of information, they can certainly improve results if combined with other techniques.
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JALAN, ADITYA HRIDAY. "ASSESSING SEVERITY OF SOFTWARE DEFECT REPORTS USING MACHINE LEARNING TECHNIQUES." Thesis, 2014. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15606.

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Анотація:
As per the software development, software testing is one of the most important phases of software life cycle. And similarly, a defect report is a key document which is required for software testing. We need to maintain testing reports and defect reports to keep track of the behaviour of software, whether it is going on as desired or we need to make changes in the undergoing software development. But as the software complexity increases, the number of defects also increases. Our prime focus then relies on looking for the defects and classifying them on the basis of severity. Severity assessment is of prime focus for test engineers. Actually, most of the defect reports generated by almost any kind of software tool generate a log report. Such log reports contain description of the defects encountered. It is difficult to scan each and every line and find out the severity of the defects. So, there is a need for a system that scans various log reports and classifies it in various categories as low, medium, high on the basis of keywords encountered in the defect report. The main idea behind this paper can be broadly classified in two heads, text classification and machine learning techniques. As a subject, we have chosen the NASA’s Project and Issue Tracking System (PITS) dataset and TOMCAT dataset. Various text classification techniques have been applied to extract raw data from the log report. Then, we have applied machine learning techniques over it to get the severity report. To validate the result, k-fold cross validation method is applied over data in different machine learning techniques. The machine learning technique used here is Multilayer Perceptron and statistical method used is Multinominal Logistic Regression. It has been observed that MLP method has given better results in all of the cases as compared to MLR method.
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Ivanov, E. S., та Е. С. Иванов. "Разработка методики тестирования программного обеспечения : магистерская диссертация". Master's thesis, 2014. http://hdl.handle.net/10995/28187.

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The title of graduation work is development of techniques of software testing. The objective of research is studying of the testing process, defect types in the software and their tracking, methods of creating and applying test cases, and development of auto-test project for the web service “Expert”. An additional objective is to conduct stress testing for the web service "Expert". The first part is devoted to the theoretical foundations of testing: a place of testing in software development, testing process in it-companies, review of defects and their tracking, and techniques of creating tests and their applying. The second part is devoted to the review of software for load testing and it’s practical usage for testing the web service “Expert”. The last part is devoted to the study of automation functional testing and development of the auto test project for the web service “Expert”. The graduation work consists of an introduction, 12 chapters and conclusion on 106 pages, including 55 figures and the list of 15 references.
Тема выпускной квалификационное работы: разработка методики тестирования программного обеспечения. Цель работы: изучение процесса тестирования, видов дефектов в ПО и их отслеживание, способов создания и применения тест кейсов, и, на основе полученных знаний, разработка проекта авто-тестов для веб-сервиса "Эксперт". Дополнительной целью является проведение нагрузочного тестирования для веб-сервиса "Эксперт". Первая часть работы посвящена теоретическим основам тестирования: место тестирования в разработке ПО, процесс тестирования в IT-компаниях, обзор дефектов, способов их отслеживания, а также техник создания тестов и их применение. Вторая часть посвящена обзору ПО для нагрузочного тестирования и его практическое использование для тестирования веб-сервиса «Эксперт». Третья часть посвящена изучению процесса автоматизации функционального тестирования и разработке авто-тестов для веб-сервиса «Эксперт». Выпускная работа состоит из введения, 12 глав и заключения, изложенных на 106 страницах, а также списка литературы и приложений. В работе имеется 55 рисунков. Список литературы содержит 15 наименований.
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Частини книг з теми "SOFTWARE DEFECT REPORTS"

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Gromova, Anna. "Using Cluster Analysis for Characteristics Detection in Software Defect Reports." In Lecture Notes in Computer Science, 152–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73013-4_14.

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Wang, Han, Min Zhou, Xi Cheng, Guang Chen, and Ming Gu. "Which Defect Should Be Fixed First? Semantic Prioritization of Static Analysis Report." In Software Analysis, Testing, and Evolution, 3–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04272-1_1.

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Gou, Lang, Qing Wang, Jun Yuan, Ye Yang, Mingshu Li, and Nan Jiang. "Quantitatively Managing Defects for Iterative Projects: An Industrial Experience Report in China." In Making Globally Distributed Software Development a Success Story, 369–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79588-9_32.

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Wee Land, Lesley Pek, Chris Sauer, and Ross Jeffery. "Validating the defect detection performance advantage of group designs for software reviews: Report of a laboratory experiment using program code." In Lecture Notes in Computer Science, 294–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63531-9_21.

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Jarzabek, Stanislaw, and Cezary Boldak. "Prioritizing Defects for Debugging with Requirement-to-Test-Case Mappings." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220254.

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Анотація:
Suppose regression testing reported many defects, and now we need decide about the order in which to correct them. In addition to commonly used defect prioritization based on their business importance, we propose to take into account also dependencies among defects, and to correct defects in an order that reduces the overall debugging effort. A goal here is to start by fixing root causes of failures, i.e., defects that may be causing many other program failures. A related goal is to avoid prematurely fixing defects that depend on other, yet to be fixed defects, as this is likely to incur wastage of time. Our proposed method requires that test cases have been mapped to relevant software requirements. We defined heuristics to infer defect dependencies, and a suitable defect debugging order from these mappings. The process is semi-automatic, supported by a tool called TRAcker. TRAcker accepts test results, performs heuristics-based computations, and recommends a time-efficient defect debugging order from the perspective of defect dependencies. TRAcker’s filtering and visualization features allow a user to participate in the process, so that tool recommendations as well as other factors can be taken into account. We show that defect prioritization on technical and business grounds together contribute to effective debugging.
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Vimaladevi M. and Zayaraz G. "A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks." In Research Anthology on Agile Software, Software Development, and Testing, 1701–19. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3702-5.ch081.

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The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.
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Sapna, P. G., Hrushikesha Mohanty, and Arunkumar Balakrishnan. "Consistency Checking of Specification in UML." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 300–316. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4494-6.ch014.

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Анотація:
The increasing use of software is giving rise to the development of highly complex software systems. Further, software systems are required to be of high quality as a defect can have catastrophic effect on business as well as human life. Testing is defined as the process of executing a program with the intention of finding errors. Software testing is an expensive process of the software development life cycle consuming nearly 50% of development cost. Software testing aims not only to guarantee consistency in software specification but also to validate its implementation meeting user requirements. On the whole, it is observed that in general, errors in software systems set in at the early stages of the software development cycle (i.e. while gathering user requirements and deciding on specification of intended software). Even though formal specification in B and Z assures a provable system, its use has become less popular due to mathematical rigor. The Unified Modeling Language (UML), a semi-formal language with graphical notations consisting of various diagrams has caught software developers’ imaginations and, it has become popular in industry. UML, with its several diagrams, helps to develop a model of intended software, and the model behaviour is simulated and tested to the satisfaction of both developer as well as users. As a UML model includes specifications of different aspects of a software system through several diagrams, it is essential to maintain consistency among diagrams so that quality of the model is maintained, and through inconsistency checking and removal, the model moves toward completeness. The works reported in literature on this topic are reviewed here.
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Тези доповідей конференцій з теми "SOFTWARE DEFECT REPORTS"

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Menzies, Tim, and Andrian Marcus. "Automated severity assessment of software defect reports." In 2008 IEEE International Conference on Software Maintenance (ICSM). IEEE, 2008. http://dx.doi.org/10.1109/icsm.2008.4658083.

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Patil, Sangameshwar. "Concept-Based Classification of Software Defect Reports." In 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR). IEEE, 2017. http://dx.doi.org/10.1109/msr.2017.20.

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Jindal, Rajni, Ruchika Malhotra, and Abha Jain. "Mining defect reports for predicting software maintenance effort." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275620.

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Garousi, Vahid, Ebru Göçmen Ergezer, and Kadir Herkiloğlu. "Usage, usefulness and quality of defect reports." In EASE '16: 20th International Conference on Evaluation and Assessment in Software Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2915970.2916009.

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Runeson, Per, Magnus Alexandersson, and Oskar Nyholm. "Detection of Duplicate Defect Reports Using Natural Language Processing." In 29th International Conference on Software Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icse.2007.32.

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Lai, Tuan Dung. "Towards the generation of machine learning defect reports." In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2021. http://dx.doi.org/10.1109/ase51524.2021.9678592.

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Mellegard, Niklas, Hakan Burden, Daniel Levin, Kenneth Lind, and Ana Magazinius. "Contrasting Big Bang with Continuous Integration through Defect Reports." In 2021 IEEE 18th International Conference on Software Architecture Companion (ICSA-C). IEEE, 2021. http://dx.doi.org/10.1109/icsa-c52384.2021.00010.

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Gromova, Anna, Iosif Itkin, Sergey Pavlov, and Alexander Korovayev. "Raising the Quality of Bug Reports by Predicting Software Defect Indicators." In 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2019. http://dx.doi.org/10.1109/qrs-c.2019.00048.

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Yusop, Nor Shahida Mohamad, Jean-Guy Schneider, John Grundy, and Rajesh Vasa. "Analysis of the Textual Content of Mined Open Source Usability Defect Reports." In 2017 24th Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2017. http://dx.doi.org/10.1109/apsec.2017.42.

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Mellegård, Niklas. "Using weekly open defect reports as an indicator for software process efficiency." In IWSM/Mensura '17: 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3143434.3143463.

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Звіти організацій з теми "SOFTWARE DEFECT REPORTS"

1

Leis, Brian. L51794A Failure Criterion for Residual Strength of Corrosion Defects in Moderate to High Toughness Pipe. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 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 different variables on the behavior of blunt corrosion defects and to develop an equation to reliably and conservatively predict failure of corrosion defects in moderate- to high-toughness steels. The PCORR software and the PCORRC equation have been compared against the experimental database and have been shown to reduce excess conservatism in predicting failure of actual corrosion defects that were likely to have been controlled by the plastic collapse mechanism. Because of the general nature and theoretical foundation of these developments, both the software tool and the equation can be extended in future work to develop similar criteria for combinations of defects and loadings not addressed by this version of the PCORRC equation such as interaction of separated adjacent defects and axial loads on defects.
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Lane, Lerose, and DingXin Cheng. Pavement Condition Survey using Drone Technology. Mineta Transportation Institute, June 2023. http://dx.doi.org/10.31979/mti.2023.2202.

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Timely repairs of pavement defects are essential in protecting both public road and highway systems. Identification of pavement distresses is necessary for planning pavement repairs. This has previously been performed by engineers surveying the roadways visually in the field. As drone usage has progressed, it has become clear that drones are a valuable tool to enhance visual documentation, improve project communication, and provide various data for processing. The use of drone technology has improved both the speed and accuracy of capturing data. Available software has allowed the data to be processed and analyzed in an office environment. This report summarizes the use of drone technology for pavement evaluation for three case studies. Results from this study can be used to deepen understanding of drone use in the process of data gathering for timely repairs for transportation infrastructure.
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