Literatura académica sobre el tema "SOFTWARE DEFECT REPORTS"
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Artículos de revistas sobre el tema "SOFTWARE DEFECT REPORTS"
Jindal, Rajni, Ruchika Malhotra y Abha Jain. "Predicting Software Maintenance Effort by Mining Software Project Reports Using Inter-Version Validation". International Journal of Reliability, Quality and Safety Engineering 23, n.º 06 (diciembre de 2016): 1640009. http://dx.doi.org/10.1142/s021853931640009x.
Texto completoMalhotra, Ruchika, Nidhi Kapoor, Rishabh Jain y Sahaj Biyani. "Severity Assessment of Software Defect Reports using Text Classification". International Journal of Computer Applications 83, n.º 11 (18 de diciembre de 2013): 13–16. http://dx.doi.org/10.5120/14492-2622.
Texto completoJindal, Rajni, Ruchika Malhotra y Abha Jain. "Prediction of defect severity by mining software project reports". International Journal of System Assurance Engineering and Management 8, n.º 2 (10 de marzo de 2016): 334–51. http://dx.doi.org/10.1007/s13198-016-0438-y.
Texto completoMarappan, Shanmugasundaram, Archana Kollu, Ismail Keshta, Shehab Mohamed Beram, Sahil Bhende y Karthikeyan Kaliyaperumal. "An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software". Scientific Programming 2022 (15 de septiembre de 2022): 1–10. http://dx.doi.org/10.1155/2022/2302027.
Texto completoMellegard, Niklas, Hakan Burden, Daniel Levin, Kenneth Lind y Ana Magazinius. "Contrasting Big Bang With Continuous Integration Through Defect Reports". IEEE Software 37, n.º 3 (mayo de 2020): 14–20. http://dx.doi.org/10.1109/ms.2018.2880822.
Texto completoSultan, Torky, Ayman E. Khedr y Mostafa Sayed. "A Proposed Defect Tracking Model for Classifying the Inserted Defect Reports to Enhance Software Quality Control". International Journal of Computer Applications 67, n.º 14 (18 de abril de 2013): 1–7. http://dx.doi.org/10.5120/11460-7068.
Texto completoSultan, Torky, Ayman Khedr y Mostafa Sayed. "A Proposed Defect Tracking Model for Classifying the Inserted Defect Reports to Enhance Software Quality Control". Acta Informatica Medica 21, n.º 2 (2013): 103. http://dx.doi.org/10.5455/aim.2013.21.103-108.
Texto completoYadla, Suresh, Jane Huffman Hayes y Alex Dekhtyar. "Tracing requirements to defect reports: an application of information retrieval techniques". Innovations in Systems and Software Engineering 1, n.º 2 (29 de julio de 2005): 116–24. http://dx.doi.org/10.1007/s11334-005-0011-3.
Texto completoPipitone, J. y S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models". Geoscientific Model Development 5, n.º 4 (9 de agosto de 2012): 1009–22. http://dx.doi.org/10.5194/gmd-5-1009-2012.
Texto completoPipitone, J. y S. Easterbrook. "Assessing climate model software quality: a defect density analysis of three models". Geoscientific Model Development Discussions 5, n.º 1 (15 de febrero de 2012): 347–82. http://dx.doi.org/10.5194/gmdd-5-347-2012.
Texto completoTesis sobre el tema "SOFTWARE DEFECT REPORTS"
Ye, Xin. "Automated Software Defect Localization". Ohio University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1462374079.
Texto completoCAVALCANTI, 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.
Texto completoMade 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.
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.
Texto completoIvanov, E. S. y Е. С. Иванов. "Разработка методики тестирования программного обеспечения : магистерская диссертация". Master's thesis, 2014. http://hdl.handle.net/10995/28187.
Texto completoТема 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.
Capítulos de libros sobre el tema "SOFTWARE DEFECT REPORTS"
Gromova, Anna. "Using Cluster Analysis for Characteristics Detection in Software Defect Reports". En Lecture Notes in Computer Science, 152–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73013-4_14.
Texto completoWang, Han, Min Zhou, Xi Cheng, Guang Chen y Ming Gu. "Which Defect Should Be Fixed First? Semantic Prioritization of Static Analysis Report". En Software Analysis, Testing, and Evolution, 3–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04272-1_1.
Texto completoGou, Lang, Qing Wang, Jun Yuan, Ye Yang, Mingshu Li y Nan Jiang. "Quantitatively Managing Defects for Iterative Projects: An Industrial Experience Report in China". En 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.
Texto completoWee Land, Lesley Pek, Chris Sauer y Ross Jeffery. "Validating the defect detection performance advantage of group designs for software reviews: Report of a laboratory experiment using program code". En Lecture Notes in Computer Science, 294–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63531-9_21.
Texto completoJarzabek, Stanislaw y Cezary Boldak. "Prioritizing Defects for Debugging with Requirement-to-Test-Case Mappings". En Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220254.
Texto completoVimaladevi M. y Zayaraz G. "A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks". En 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.
Texto completoSapna, P. G., Hrushikesha Mohanty y Arunkumar Balakrishnan. "Consistency Checking of Specification in UML". En 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.
Texto completoActas de conferencias sobre el tema "SOFTWARE DEFECT REPORTS"
Menzies, Tim y Andrian Marcus. "Automated severity assessment of software defect reports". En 2008 IEEE International Conference on Software Maintenance (ICSM). IEEE, 2008. http://dx.doi.org/10.1109/icsm.2008.4658083.
Texto completoPatil, Sangameshwar. "Concept-Based Classification of Software Defect Reports". En 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR). IEEE, 2017. http://dx.doi.org/10.1109/msr.2017.20.
Texto completoJindal, Rajni, Ruchika Malhotra y Abha Jain. "Mining defect reports for predicting software maintenance effort". En 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275620.
Texto completoGarousi, Vahid, Ebru Göçmen Ergezer y Kadir Herkiloğlu. "Usage, usefulness and quality of defect reports". En 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.
Texto completoRuneson, Per, Magnus Alexandersson y Oskar Nyholm. "Detection of Duplicate Defect Reports Using Natural Language Processing". En 29th International Conference on Software Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icse.2007.32.
Texto completoLai, Tuan Dung. "Towards the generation of machine learning defect reports". En 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2021. http://dx.doi.org/10.1109/ase51524.2021.9678592.
Texto completoMellegard, Niklas, Hakan Burden, Daniel Levin, Kenneth Lind y Ana Magazinius. "Contrasting Big Bang with Continuous Integration through Defect Reports". En 2021 IEEE 18th International Conference on Software Architecture Companion (ICSA-C). IEEE, 2021. http://dx.doi.org/10.1109/icsa-c52384.2021.00010.
Texto completoGromova, Anna, Iosif Itkin, Sergey Pavlov y Alexander Korovayev. "Raising the Quality of Bug Reports by Predicting Software Defect Indicators". En 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.
Texto completoYusop, Nor Shahida Mohamad, Jean-Guy Schneider, John Grundy y Rajesh Vasa. "Analysis of the Textual Content of Mined Open Source Usability Defect Reports". En 2017 24th Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2017. http://dx.doi.org/10.1109/apsec.2017.42.
Texto completoMellegård, Niklas. "Using weekly open defect reports as an indicator for software process efficiency". En 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.
Texto completoInformes sobre el tema "SOFTWARE DEFECT REPORTS"
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), enero de 2000. http://dx.doi.org/10.55274/r0011253.
Texto completoLane, Lerose y DingXin Cheng. Pavement Condition Survey using Drone Technology. Mineta Transportation Institute, junio de 2023. http://dx.doi.org/10.31979/mti.2023.2202.
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