Academic literature on the topic 'Software defects'
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Journal articles on the topic "Software defects"
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 (February 2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.
Full textKumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (March 23, 2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.
Full textZhang, 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.
Full textHenderson, Craig. "Managing software defects." ACM SIGSOFT Software Engineering Notes 33, no. 4 (July 2008): 1–3. http://dx.doi.org/10.1145/1384139.1384141.
Full textHan, 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.
Full textHuh, Sang Moo, and Woo-Je Kim. "The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects." Applied Sciences 10, no. 19 (October 4, 2020): 6946. http://dx.doi.org/10.3390/app10196946.
Full textKumaresh, Sakthi, and R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (October 15, 2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.
Full textPark, Jihyun, and Byoungju Choi. "Automatic Method for Distinguishing Hardware and Software Faults Based on Software Execution Data and Hardware Performance Counters." Electronics 9, no. 11 (November 2, 2020): 1815. http://dx.doi.org/10.3390/electronics9111815.
Full textFalessi, Davide, Aalok Ahluwalia, and Massimiliano DI Penta. "The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects." ACM Transactions on Software Engineering and Methodology 31, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3467895.
Full textPagadala, Srivyshnavi, Sony Bathala, and B. Uma. "An Efficient Predictive Paradigm for Software Reliability." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 114–16. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2051.
Full textDissertations / Theses on the topic "Software defects"
Couto, César Francisco de Moura. "Predicting software defects with causality tests = Predizendo defeitos de software com testes de causalidade." Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/ESBF-9GMMLN.
Full textPredição de defeitos é uma área de pesquisa em engenharia de software que objetiva identificar os componentes de um sistema de software que são mais prováveis de apresentar defeitos. Apesar do grande investimento em pesquisa objetivando identificar uma maneira efetiva para predizer defeitos em sistemas de software, ainda não existe uma solução amplamente utilizada para este problema. As atuais abordagens para predição de defeitos apresentam pelo menos dois problemas principais. Primeiro, a maioria das abordagens não considera a idéia de causalidade entre métricas de software e defeitos. Mais especificamente, os estudos realizados para avaliar as técnicas de predição de defeitos não investigam em profundidade se as relações descobertas indicam relações de causa e efeito ou se são coincidências estatísticas. O segundo problema diz respeito a saída dos atuais modelos de predição de defeitos. Tipicamente, a maioria dos modelos indica o número ou a existência de defeitos em um componente no futuro. Claramente, a disponibilidade desta informação é importante para promover a qualidade de software. Entretanto, predizer defeitos logo que eles são introduzidos no código é mais útil para mantenedores que simplesmente sinalizar futuras ocorrências de defeitos. Para resolver estas questões, nós propomos uma abordagem para predição de defeitos centrada em evidências mais robustas no sentido de causalidade entre métricas de código fonte (como preditor) e a ocorrência de defeitos. Mais especificamente, nós usamos um teste de hipótese estatístico proposto por Clive Granger (Teste de Causalidade de Granger) para avaliar se variações passadas nos valores de métricas de código fonte podem ser usados para predizer mudanças em séries temporais de defeitos. Nossa abordagem ativa alarmes quando mudanças realizadas no código fonte de um sistema alvo são prováveis de produzir defeitos. Nós avaliamos nossa abordagem em várias fases da vida de quatro sistemas implementados em Java. Nós alcançamos um precisão média maior do que 50% em três dos quatro sistemas avaliados. Além disso, ao comparar nossa abordagem com abordagens que não são baseadas em testes de causalidade, nossa abordagem alcançou uma precisão melhor.
Wang, Hui. "Software Defects Classification Prediction Based On Mining Software Repository." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-216554.
Full textNakamura, Taiga. "Recurring software defects in high end computing." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/7217.
Full textThesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Hickman, Björn, and Victor Holmqvist. "Predict future software defects through machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301864.
Full textRapportens mål var att undersöka potentiella effekter av att predicera mjukvarudefekter i ett mjukvaruprojekt. Detta genomfördes med hjälp av maskininlärning. Vidare undersöker studien vilka särdrag hos en kodbas som är av intresse för att genomföra dessa prediktioner. De undersökta särdrag som användes för att träna modellerna var av både teknisk såväl som organisatorisk karaktär. Modellerna som användes var Random forest, logistisk regression och naive Bayes. Data hämtades från ett open source git-repository, VSCode, där korrekta klassificeringar av rapporterade defekter hämtades från GitHub-Issues. Rapportens resultat ger indikationer på att både tekniska och organisatoriska särdrag är av relevans. Samtliga tre modeller påvisade liknande resultat. Vidare kan modellernas resultat visa stöd för att användas som ett komplementärt verktyg vid projektledning av mjukvaruprojekt. Närmare bestämt stöd vid riskplanering, riskbedömning och vid resursallokering. Vidare skulle fortsatta studier inom detta område vara av intresse för att bekräfta denna studies slutsatser.
Shippey, Thomas Joshua. "Exploiting abstract syntax trees to locate software defects." Thesis, University of Hertfordshire, 2015. http://hdl.handle.net/2299/16365.
Full textZheng, Xue Lin. "A Framework for Early Detection of Requirements Defects." Thesis, Griffith University, 2008. http://hdl.handle.net/10072/366377.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
Full Text
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.
Full textAlmossawi, Ali. "Investigating the architectural drivers of defects in open-source software systems : an empirical study of defects and reopened defects in GNOME." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76566.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 64-67).
In major software systems that are developed by competent software engineers, the existence of defects in production is unlikely to be an acceptable situation. And yet, we find that in several such systems, defects remain a reality. Furthermore, the number of changes that are fixed only to then be reopened is noticeable. The implications of having defects in a system can be frustrating for all stakeholders, and when they require constant rework, they can lead to the problematic code-test-code-test mode of development. For management, such conditions can result in slipped schedules and an increase in development costs and for upper management and users, they can result in losing confidence in the product. This study looks at the drivers of defects in the mature open-source project GNOME and explores the relationship between the various drivers of these defects and software quality. Using defect-activity and source-code data for 32 systems over a period of eight years, the work presents a multiple regression model capable of explaining 16.2% of defects and a logistic regression model capable of explaining between 13.6% and 18.1% of reopened defects. The study also shows that although defects in general and reopened defects appear to move together, defects in general correlate with a measure of complexity that captures how components connect to each other whereas reopened defects correlate with a measure that captures the inner complexities of components, thereby suggesting that different types of defects are correlated with different forms of complexity.
by Ali Almossawi.
S.M.in Engineering and Management
Vandehei, Bailey R. "Leveraging Defects Life-Cycle for Labeling Defective Classes." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2111.
Full textArantes, Alessandro Oliveira. "REACTOR: Combining static analysis, testing and reverse engineering to detect software defects." Instituto Nacional de Pesquisas Espaciais (INPE), 2016. http://urlib.net/sid.inpe.br/mtc-m21b/2016/04.20.19.30.
Full textÉ cada vez mais comum a utilização de sistemas computacionais em substituição à mão de obra humana em sistemas críticos, e na medida em que estes sistemas têm se tornado mais autônomos para tomar decisões, eles exigem um alto grau de qualidade e robustez. O INPE desenvolve sistemas embarcados para satélites científicos e balões estratosféricos; consequentemente, os processos de verificação e validação exigem cuidados especiais na detecção e prevenção de defeitos. E tendo em vista a complexidade e o domínio dos sistemas em questão, estes processos consomem a mão de obra especialista por um longo período. Neste cenário, a aplicação de técnicas que possam efetuar testes de forma automática auxiliam o processo proporcionando um ganho significativo de produtividade e eficácia no trabalho dos especialistas. Com esse objetivo, este trabalho realiza a engenharia reversa de código-fonte de modo a combinar dois processos de V\&V, análise estática de código fonte e teste de software, a fim de detectar uma gama mais ampla de defeitos. O método proposto, denominado REACTOR (Reverse Engineering for stAtic Code analysis and Testing to detect sOftwaRe defects), complementa a maneira tradicional pela qual os analisadores de código estático trabalham usando informações dinâmicas obtidas por um gerador de caso de teste automatizado, que combina três técnicas de caixa preta diferentes, sendo também possível inferir um conjunto de resultados esperados estimados similar a um oráculo de teste. Ainda assim, a leitura do código fonte estático por si só pode não revelar vários tipos de defeitos que só podem ser detectados combinando a análise estática com informação dinâmica. O método REACTOR foi implementado em uma ferramenta de software, também chamado de REACTOR, que poupa os testadores de um grande volume de trabalho manual automatizando o processo e baseando-se apenas no código fonte. Além disso, a REACTOR foi aplicada em alguns casos de estudo incluindo uma aplicação da área espacial, e seu desempenho foi melhor do que outras três conhecidos analisadores de código estático.
Books on the topic "Software defects"
Glitch: The hidden impact of faulty software. Upper Saddle River, NJ: Prentice Hall, 2010.
Find full textThe software conspiracy: Why software companies put out faulty products, how they can hurt you, and what you can do about it. New York: McGraw-Hill, 2000.
Find full textPerry, William E. A standard for auditing computer applications: Auditing information services defects. Boston: Auerbach, 1996.
Find full textZero defect software. New York: McGraw-Hill, 1990.
Find full textToward zero-defect programming. Reading, Mass: Addison-Wesley, 1999.
Find full textYounessi, Houman. Object-oriented defect management of software. Upper Saddle River, NJ: Prentice Hall PTR, 2002.
Find full textCai, Kai-Yuan. Software Defect and Operational Profile Modeling. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5593-3.
Full textSoftware defect and operational profile modeling. Boston: Kluwer Academic Publishers, 1998.
Find full textMiller, Ann K. Engineering quality software: Defect detection and prevention. Reading, Mass: Addison-Wesley, 1992.
Find full textPeterson, Ivars. Fatal Defect: Chasing Killer Computer Bugs. New York: Times Books, 1995.
Find full textBook chapters on the topic "Software defects"
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, 313–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10548-7_23.
Full textBeningo, Jacob. "Jump-Starting Software Development to Minimize Defects." In Embedded Software Design, 241–56. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8279-3_10.
Full textHui, Zhanwei, Song Huang, Zhengping Ren, and Yi Yao. "Review of Software Security Defects Taxonomy." In Lecture Notes in Computer Science, 310–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16248-0_46.
Full textSharma, Kanta Prasad, Vinesh Kumar, and Dac-Nhuong Le. "Defects Maintainability Prediction of the Software." In Optimization of Automated Software Testing Using Meta-Heuristic Techniques, 155–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07297-0_10.
Full textHolling, Dominik, Daniel Méndez Fernández, and Alexander Pretschner. "A Field Study on the Elicitation and Classification of Defects for Defect Models." In Product-Focused Software Process Improvement, 380–96. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26844-6_28.
Full textKessentini, Marouane, Houari Sahraoui, Mounir Boukadoum, and Manuel Wimmer. "Search-Based Design Defects Detection by Example." In Fundamental Approaches to Software Engineering, 401–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19811-3_28.
Full textMahouachi, Rim, Marouane Kessentini, and Khaled Ghedira. "A New Design Defects Classification: Marrying Detection and Correction." In Fundamental Approaches to Software Engineering, 455–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28872-2_31.
Full textHe, Lei, Juan Li, Qing Wang, and Ye Yang. "Predicting Upgrade Project Defects Based on Enhancement Requirements: An Empirical Study." In Trustworthy Software Development Processes, 268–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01680-6_25.
Full textRana, Zeeshan A., Sehrish Abdul Malik, Shafay Shamail, and Mian M. Awais. "Identifying Association between Longer Itemsets and Software Defects." In Neural Information Processing, 133–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42051-1_18.
Full textYang, Peng. "Software Defects Detecting Method Based on Data Mining." In Advances in Computer Science, Environment, Ecoinformatics, and Education, 272–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23324-1_44.
Full textConference papers on the topic "Software defects"
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.
Full textSasso, Tommaso Dal. "Managing Software Defects." In 2014 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2014. http://dx.doi.org/10.1109/icsme.2014.124.
Full textJanusz, Sosnowski, and Maciej Korpalski. "Correlating software metrics with software defects." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2018. http://dx.doi.org/10.1117/12.2501150.
Full textVescan, Andreea, Camelia Serban, and Gloria Cerasela Crisan. "Software Defects Rules Discovery." In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2021. http://dx.doi.org/10.1109/icstw52544.2021.00028.
Full textCassels, J. J. "Modeling IC Defects Using Circuit Simulation Software." In ISTFA 1996. ASM International, 1996. http://dx.doi.org/10.31399/asm.cp.istfa1996p0133.
Full textGandini, Sergio, Danilo Ravotto, Walter Ruzzarin, Ernesto Sanchez, Giovanni Squillero, and Alberto Tonda. "Automatic detection of software defects." In the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570238.
Full textWu, Binghui Helen. "Modeling defects in software systems." In 2011 IEEE International Conference on Granular Computing (GrC-2011). IEEE, 2011. http://dx.doi.org/10.1109/grc.2011.6122690.
Full textCiborowska, Agnieszka, Aleksandar Chakarov, and Rahul Pandita. "Contemporary COBOL: Developers' Perspectives on Defects and Defect Location." In 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2021. http://dx.doi.org/10.1109/icsme52107.2021.00027.
Full textYang, Zhao Hong, Yun Zhan Gong, Qing Xiao, and Ya Wen Wang. "DTS - A Software Defects Testing System." In 2008 Eighth IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2008). IEEE, 2008. http://dx.doi.org/10.1109/scam.2008.12.
Full textKour, George, Shaul Strachan, and Raz Regev. "Estimating Handling Time of Software Defects." In Fourth International Conference on Computer Science and Information Technology. Academy & Industry Research Collaboration Center (AIRCC), 2017. http://dx.doi.org/10.5121/csit.2017.70413.
Full textReports on the topic "Software defects"
Snijders, J., C. Morrow, and R. van Mook. Software Defects Considered Harmful. RFC Editor, April 2022. http://dx.doi.org/10.17487/rfc9225.
Full textFlorac, William A. Software Quality Measurement: A Framework for Counting Problems and Defects. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada258556.
Full textThomas, R. Edward. Hardwood log defect photographic database, software and user's guide. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2009. http://dx.doi.org/10.2737/nrs-gtr-40.
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