Relevant bibliographies by topics / Debugging

Academic literature on the topic 'Debugging'

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Published: 4 June 2021
Last updated: 4 May 2024

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Journal articles on the topic "Debugging"

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Chmiel, Ryan, and Michael C. Loui. "Debugging." ACM SIGCSE Bulletin 36, no. 1 (March 2004): 17–21. http://dx.doi.org/10.1145/1028174.971310.

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Murphy, Laurie, Gary Lewandowski, Renée McCauley, Beth Simon, Lynda Thomas, and Carol Zander. "Debugging." ACM SIGCSE Bulletin 40, no. 1 (February 29, 2008): 163–67. http://dx.doi.org/10.1145/1352322.1352191.

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Holmes, David. "Debugging." Lancet Neurology 10, no. 12 (December 2011): 1046–47. http://dx.doi.org/10.1016/s1474-4422(11)70258-7.

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Galatenko, V. A., and K. A. Kostyukhin. "Reversible Debugging." PROGRAMMNAYA INGENERIA 10, no. 7-8 (August 22, 2019): 291–96. http://dx.doi.org/10.17587/prin.10.291-296.

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Abramson, David, Ian Foster, John Michalakes, and Rok Sosič. "Relative debugging." Communications of the ACM 39, no. 11 (November 1996): 69–77. http://dx.doi.org/10.1145/240455.240475.

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Spinellis, Diomidis. "Differential Debugging." IEEE Software 30, no. 5 (September 2013): 19–21. http://dx.doi.org/10.1109/ms.2013.103.

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Martz, Lauren. "Debugging pathogens." Science-Business eXchange 1, no. 23 (July 2008): 544. http://dx.doi.org/10.1038/scibx.2008.544.

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Neville-Neil, George. "Debugging Devices." Queue 6, no. 7 (November 2008): 6–9. http://dx.doi.org/10.1145/1483101.1483103.

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Adler, Tina. "Debugging Blood." Science News 147, no. 6 (February 11, 1995): 92. http://dx.doi.org/10.2307/3979188.

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Shields, Robert, and Rebecca Stratford. "Debugging tomatoes." Nature 366, no. 6455 (December 1993): 508–9. http://dx.doi.org/10.1038/366508a0.

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Dissertations / Theses on the topic "Debugging"

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Scerpa, Daniel. "Debugging Reversibile." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15470/.

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Per migliorare l'efficienza del processo di debug, nel corso degli anni, è cresciuta sempre di più l’idea di mettere, a disposizione del programmatore, un debugger che consentisse non solo, di fare un passo avanti nel codice, ma anche la possibilità di ripercorrerre all’indietro l’esecuzione del programma per ridurre i costi e i tempi nel sviluppare software.
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Murphy, Toriano A. "Statistical debugging." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FMurphy_Toriano.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, March 2008.
Thesis Advisor(s): Auguston, Mikhail. "March 2008." Description based on title screen as viewed on May 5, 2008. Includes bibliographical references (p. 91). Also available in print.
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Petrillo, Fábio dos Santos. "Swarm debugging : the collective debugging intelligence of the crowd." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/150176.

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As formigas são criaturas fascinantes que, além dos avanços na biologia também inspiraram pesquisas sobre teoria da informação. Em particular, o estudo resultou na criação da Teoria da Forragem de Informação, que descreve como os agentes de buscam informações em seu ambiente. Esta teoria também explica fenômenos recentes e bem-sucedidos, como crowd sourcing. Crowdsourcing tem sido aplicado a muitas atividades em engenharia de software, incluindo desenvolvimento, tradução e testes, mas uma atividade parece resistir: depuração. No entanto, os desenvolvedores sabem que a depuração pode exigir dedicação, esforço, longas horas de trabalho, por vezes, para mudar uma linha de código único. Nós introduzimos o conceito de Depuração em Enxame, para trazer crowd sourcing para a atividade de depuração. Através de crowd sourcing, pretendemos ajudar os desenvolvedores, capitalizando a sua dedicação, esforço e longas horas de trabalho para facilitar atividades de depuração. Mostramos que a depuração enxame requer uma abordagem específica para recolher informações relevantes, e descrevemos sua infra-estrutura. Mostramos também que a depuração em enxame pode reduzir o esforço desenvolvedores. Concluímos com as vantagens e limitações atuais de depuração enxame, e sugerir caminhos para superar estas limitações e ainda mais a adoção de crowd sourcing para atividades de depuração.
Ants are fascinating creatures that beyond the advances in biology have also inspired research on information theory. In particular, their study resulted in the creation of the Information Foraging Theory, which describes how agents forages for information in their environment. This theory also explains recent and fruitful phenomena, such as crowdsourcing. Many activities in software engineering have applied crowdsourcing, including development, translation, and testing, but one action seems to resist: debugging. Developers know that debugging can require dedication, effort, long hours of work, sometimes for changing one line of code only. We introduce the concept of Swarm Debugging, to bring crowdsourcing to the activity of debugging. Through crowdsourcing, we aim at helping developers by capitalizing on their dedication, effort, and long hours of work to ease debugging activities of their peers or theirs, on other bugs. We show that swarm debugging requires a particular approach to collect relevant information, and we describe the Swarm Debugging Infrastructure. We also show that swarm debugging minimizes developers effort. We conclude with the advantages and current limitations of swarm debugging and suggest directions to overcome these limitations and further the adoption of crowdsourcing for debugging activities.
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Searle, Aaron James. "Automatic relative debugging." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16445/1/Aaron_Searle_Thesis.pdf.

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Relative Debugging is a paradigm that assists users to locate errors in programs that have been corrected or enhanced. In particular, the contents of key data structures in the development version are compared with the contents of the corresponding data structures, in an existing version, as the two programs execute. If the values of two corresponding data structures differ at points where they should not, an error may exist and the user is notified. Relative Debugging requires users to identify the corresponding data structures within the two programs, and the locations at which the comparisons should be performed. To quickly and effectively identify useful data structures and comparison points requires that users have a detailed knowledge of the two programs under consideration. Without a detailed knowledge of the two programs, the task of locating useful data structures and comparison points can quickly become a difficult and time consuming process. Prior to the research detailed in this thesis, the Relative Debugging paradigm did not provide any assistance that allowed users to quickly and effectively identify suitable data structures and program points that will help discover the source of an error. Our research efforts have been directed at enhancing the Relative Debugging paradigm. The outcome of this research is the discovery of techniques that empower Relative Debugging users to become more productive and allow the Relative Debugging paradigm to be significantly enhanced. Specifically, the research has resulted in the following three contributions: 1. A Systematic Approach to Relative Debugging. 2. Data Flow Browsing for Relative Debugging. 3. Automatic Relative Debugging. These contributions have enhanced the Relative Debugging paradigm and allow errors to be localized with little human interaction. Minimizing the user's involvement reduces the cost of debugging programs that have been corrected or enhanced, and has a significant impact on current debugging practices.
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Searle, Aaron James. "Automatic relative debugging." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16445/.

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Relative Debugging is a paradigm that assists users to locate errors in programs that have been corrected or enhanced. In particular, the contents of key data structures in the development version are compared with the contents of the corresponding data structures, in an existing version, as the two programs execute. If the values of two corresponding data structures differ at points where they should not, an error may exist and the user is notified. Relative Debugging requires users to identify the corresponding data structures within the two programs, and the locations at which the comparisons should be performed. To quickly and effectively identify useful data structures and comparison points requires that users have a detailed knowledge of the two programs under consideration. Without a detailed knowledge of the two programs, the task of locating useful data structures and comparison points can quickly become a difficult and time consuming process. Prior to the research detailed in this thesis, the Relative Debugging paradigm did not provide any assistance that allowed users to quickly and effectively identify suitable data structures and program points that will help discover the source of an error. Our research efforts have been directed at enhancing the Relative Debugging paradigm. The outcome of this research is the discovery of techniques that empower Relative Debugging users to become more productive and allow the Relative Debugging paradigm to be significantly enhanced. Specifically, the research has resulted in the following three contributions: 1. A Systematic Approach to Relative Debugging. 2. Data Flow Browsing for Relative Debugging. 3. Automatic Relative Debugging. These contributions have enhanced the Relative Debugging paradigm and allow errors to be localized with little human interaction. Minimizing the user's involvement reduces the cost of debugging programs that have been corrected or enhanced, and has a significant impact on current debugging practices.
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Müller, Thomas. "OpenAFS Fileserver Debugging/Tuning." Universitätsbibliothek Chemnitz, 2003. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200301305.

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Unterlagen zu einem Tutorium im Rahmen des AFS-Workshops 2003 am DESY Zeuthen. Die Suche von Fehlern in komplexen Systemen setzt immer voraus, dass der korrekte Zustand des Systems bekannt ist. Denn nur auf diese Art und Weise kann man erkennen, dass es sich in einer konkreten Situation um ein Fehlverhalten handelt. In diesem Tutorium werden daher Normal- oder Sollzustände von OpenAFS-Fileservern beschrieben. Es werden einzelne Datenstrukturen dargestellt und am Beispiel der Callback-Verwaltung des Fileservers wird gezeigt, wie diese Datenstrukturen zur Laufzeit des Servers organisiert werden.
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Pothier, Guillaume. "Towards Practical Omniscient Debugging." Tesis, Universidad de Chile, 2011. http://www.repositorio.uchile.cl/handle/2250/102687.

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Binks, Dominic Frank Julian. "Declarative debugging in Gödel." Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296587.

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Mutti, Danilo. "Coverage based debugging visualization." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-15122014-230109/.

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Fault localization is a costly task in the debugging process. Usually, developers analyze failing test cases to search for faults in the programs code. Visualization techniques have been proposed to help developers grasp the source code and focus their attention onto locations more likely to contain bugs. In general, these techniques utilize two-dimensional visualization approaches. We introduce a three-dimentional visual metaphor, called CodeForest, which represents a software as a cacti forest. In the CodeForest, nodes (sets of statements executed in sequence) are thorns, methods are branches, and classes are cacti. Heuristicsbased on the frequency that lines of codes are executed in successful and failing test cases are used to assign suspiciousness values to the elements (thorns, branches, and cacti) of the forest. The new metaphor was implemented as a plug-in targeted to the Eclipse Platform. This plug-in includes the mapping of suspiciousness values to elements of a virtual forest, a parameterized trimmer, which filters elements based on their score or text, and a list of most suspicious methods (also known as \"roadmap\"), to guide the developer on his/her debugging session. An exploratory experiment was conducted; the results indicates that the tool supports developers with and without experience. Users with low or no experience utilized the roadmap and the virtual 3D environment to investigate the defect. More experienced users prefer to use the roadmap as a guide to narrow which parts of the source code should be explored.
Localizar falhas é uma tarefa custosa do processo de depuração. Normalmente, os desenvolvedores analisam casos de teste que falham para procurar por defeitos no código fonte de um programa. Técnicas de visualização têm sido propostas para ajudar os desenvolvedores a entender o código fonte e focar sua atenção nos locais com a maior probabilidade de conterem defeitos. Geralmente, essas técnicas utilizam abordagens de visualização bidimensional. Nesse trabalho é introduzida uma metáfora visual em três dimensões, chamada CodeForest, que representa um programa como uma floresta de cactus. Na CodeForest, nós (conjunto de comandos executados em sequência) são representados como espinhos, métodos como galhos e classes como troncos. Para associar valores de suspeição aos elementos da floresta (espinhos, galhos e troncos) utilizam-se heurísticas, baseadas na frequência com que linhas de código são executadas em casos de teste finalizados com sucesso e com falha. A nova metáfora foi implementada como um complemento da plataforma Eclipse de desenvolvimento de programas. Esse complemento inclui o mapeamento dos valores de suspeição para elementos de uma floresta, uma ferramenta de poda parametrizada - que filtra elementos com base em seu texto e valor de suspeição - e uma lista dos métodos mais suspeitos (conhecida como roteiro) para guiar o desenvolvedor em sua sessão de depuração. Um experimento exploratório foi conduzido e os resultados indicam que a ferramenta apoia a tarefa de depuração tanto de desenvolvedores experientes quanto inexperientes. Usuários com pouca ou nenhuma experiência utilizaram o roteiro e o ambiente virtual 3D para investigar o defeito. Usuários mais experientes preferiram utilizar o roteiro como um guia para restringir quais partes do código fonte deveriam ser exploradas.
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Yamane, Yoshito. "Event query based debugging /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6958.

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Books on the topic "Debugging"

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ill, Thompson Arthur 1951, ed. Debugging ROVER. New York: Dodd, Mead, 1985.

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Debugging C. Indianapolis, Ind: Que Corp., 1986.

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Lencevicius, Raimondas. Advanced Debugging Methods. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4419-8774-7.

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Himschoot, Peter. Practical Blazor Debugging. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6592-5.

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Christopher, Hallinan, ed. Debugging embedded Linux. [Upper Saddle River, N.J.]: Prentice Hall, 2006.

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Lencevicius, Raimondas. Advanced debugging methods. Boston, MA: Kluwer Academic, 2000.

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Hewardt, Mario. Advanced .NET debugging. Upper Saddle River, NJ: Addison-Wesley, 2010.

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Daniel, Pravat, ed. Advanced windows debugging. Upper Saddle River, NJ: Addison-Wesley, 2008.

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John, Robbins. Debugging applications: Microsoft. Redmond, WA: Microsoft Press, 2000.

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Debugging Linux systems. Boston, Mass: Prentice Hall, 2010.

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Book chapters on the topic "Debugging"

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Kaier, Ekkehard. "Debugging." In Turbo Pascal Wegweiser für Ausbildung und Studium, 147–56. Wiesbaden: Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-322-93945-6_13.

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Wielenga, Geertjan. "Debugging." In Beginning NetBeans IDE, 181–208. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1257-8_8.

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Cowell, John. "Debugging." In Essential Visual Basic 5.0 Fast, 98–106. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0983-9_11.

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Hagos, Ted. "Debugging." In Android Studio IDE Quick Reference, 41–49. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4953-6_4.

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Hardman, Casey. "Debugging." In Game Programming with Unity and C#, 129–37. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5656-5_12.

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Rudd, Anthony S. "Debugging." In Practical Usage of MVS REXX, 108–21. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3376-6_9.

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Cowell, John. "Debugging." In Essential Visual Basic 6.0 fast, 140–50. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-3417-6_13.

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Hagos, Ted. "Debugging." In Learn Android Studio 4, 147–56. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5937-5_12.

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Heller, Daniel. "Debugging." In Building a Career in Software, 207–13. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6147-7_17.

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Beecher, Karl. "Debugging." In Bad Programming Practices 101, 189–201. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3411-2_11.

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Conference papers on the topic "Debugging"

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Zeller, Andreas. "Debugging debugging." In the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1595696.1595736.

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Tam, Kinsun. "Debugging Debugging." In 2011 IEEE 35th IEEE Annual Computer Software and Applications Conference Workshops (COMPSACW). IEEE, 2011. http://dx.doi.org/10.1109/compsacw.2011.93.

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Bottcher, Axel, Veronika Thurner, Kathrin Schlierkamp, and Daniela Zehetmeier. "Debugging students' debugging process." In 2016 IEEE Frontiers in Education Conference (FIE). IEEE, 2016. http://dx.doi.org/10.1109/fie.2016.7757447.

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Chmiel, Ryan, and Michael C. Loui. "Debugging." In the 35th SIGCSE technical symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/971300.971310.

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Murphy, Laurie, Gary Lewandowski, Renée McCauley, Beth Simon, Lynda Thomas, and Carol Zander. "Debugging." In the 39th SIGCSE technical symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1352135.1352191.

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Gao, Shang, Qian Lin, Mingyuan Xia, Miao Yu, Zhengwei Qi, and Haibing Guan. "Debugging classification and anti-debugging strategies." In Fourth International Conference on Machine Vision (ICMV 11), edited by Zhu Zeng and Yuting Li. SPIE, 2011. http://dx.doi.org/10.1117/12.924835.

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Voas, J., and K. Miller. "One in a baker's dozen: debugging debugging." In 2007 IEEE International Symposium on High Assurance Systems Engineering. IEEE, 2007. http://dx.doi.org/10.1109/hase.2007.74.

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Voas, Jeffrey, and Keith Miller. "One in a Baker's Dozen: Debugging Debugging." In 10th IEEE High Assurance Systems Engineering Symposium (HASE'07). IEEE, 2007. http://dx.doi.org/10.1109/hase.2007.80.

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Estler, H. Christian, Martin Nordio, Carlo A. Furia, and Bertrand Meyer. "Collaborative Debugging." In 2013 IEEE 8th International Conference on Global Software Engineering (ICGSE). IEEE, 2013. http://dx.doi.org/10.1109/icgse.2013.21.

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Zheng, Alice X., Michael I. Jordan, Ben Liblit, Mayur Naik, and Alex Aiken. "Statistical debugging." In the 23rd international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1143844.1143983.

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Reports on the topic "Debugging"

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Budge, Kent Grimmett. Debugging Computer Code. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1457284.

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Romao, A. Tools for DNS debugging. RFC Editor, November 1994. http://dx.doi.org/10.17487/rfc1713.

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Coffrin, Carleton James. Debugging Your Quantum Computation. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1343695.

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Bailey, M., D. Socha, and D. Notkin. Parallel Debugging Using Graphical Views. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada197197.

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Lee, Yuh-jeng. A Knowledge Based Approach to Program Debugging. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada214941.

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Van De Vanter, Michael L. Error Management and Debugging in Pan I. Fort Belvoir, VA: Defense Technical Information Center, December 1989. http://dx.doi.org/10.21236/ada632165.

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Miller, Barton. Lightweight and Statistical Techniques for Petascale PetaScale Debugging. Office of Scientific and Technical Information (OSTI), June 2014. http://dx.doi.org/10.2172/1135799.

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Pearce, Lauren. Debugging and Yara Rules: Malware Analysis Day 4. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1457290.

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Liblit, B. Final Report on Statistical Debugging for Petascale Environments. Office of Scientific and Technical Information (OSTI), January 2013. http://dx.doi.org/10.2172/1062211.

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Antypas, K. B. Allinea DDT as a Parallel Debugging Alternative to Totalview. Office of Scientific and Technical Information (OSTI), March 2007. http://dx.doi.org/10.2172/923651.

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