Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation

ilustraciones, gráficas

Autores:: Rodriguez Caicedo, Alvaro Dario

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation
dc.title.translated.spa.fl_str_mv	Entendiendo e implementando redes neuronales profundas para la generación no condicionada de código fuente
title	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation
spellingShingle	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Computadores Procesamiento de la información Computers Information processing Open-ended Code Generation ML Interpretability Language Models Autoregressive Models Neural Networks Interpretabilidad de aprendizaje automático Generación No-Condicionada de Código Modelos de Lenguaje Modelos Autoregresivos Redes Neuronales
title_short	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation
title_full	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation
title_fullStr	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation
title_full_unstemmed	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation
title_sort	Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation
dc.creator.fl_str_mv	Rodriguez Caicedo, Alvaro Dario
dc.contributor.advisor.none.fl_str_mv	Gómez Perdomo, Jonatan (Thesis advisor) Nader Palacio, David Alberto (Thesis co-advisor)
dc.contributor.author.none.fl_str_mv	Rodriguez Caicedo, Alvaro Dario
dc.contributor.researchgroup.spa.fl_str_mv	Alife: Grupo de Investigación en Vida Artificial
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Computadores Procesamiento de la información Computers Information processing Open-ended Code Generation ML Interpretability Language Models Autoregressive Models Neural Networks Interpretabilidad de aprendizaje automático Generación No-Condicionada de Código Modelos de Lenguaje Modelos Autoregresivos Redes Neuronales
dc.subject.lemb.spa.fl_str_mv	Computadores Procesamiento de la información
dc.subject.lemb.eng.fl_str_mv	Computers Information processing
dc.subject.proposal.eng.fl_str_mv	Open-ended Code Generation ML Interpretability Language Models Autoregressive Models Neural Networks
dc.subject.proposal.spa.fl_str_mv	Interpretabilidad de aprendizaje automático Generación No-Condicionada de Código Modelos de Lenguaje Modelos Autoregresivos Redes Neuronales
description	ilustraciones, gráficas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-10-25T15:11:16Z
dc.date.available.none.fl_str_mv	2022-10-25T15:11:16Z
dc.date.issued.none.fl_str_mv	2022-07-15
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/82449
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82449 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Perdomo, Jonatan (Thesis advisor)f5b12a1f33e4f80f2f647b22bf161ea4600Nader Palacio, David Alberto (Thesis co-advisor)bf0ec06adf5d6b30bb46cccd07e19940Rodriguez Caicedo, Alvaro Dario40498166b5e028c422c2d2cbb408d3f4Alife: Grupo de Investigación en Vida Artificial2022-10-25T15:11:16Z2022-10-25T15:11:16Z2022-07-15https://repositorio.unal.edu.co/handle/unal/82449Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficasCode Generation is a relevant problem in computer science, supporting the automation of tasks such as code completion, program synthesis, and program translation. In recent years, Deep Learning approaches have gained popularity in the code generation problem, and some of these approaches leverage Language Models. However, the existing studies mainly focus on evaluation using machine learning metrics. Additionally, the generation process can be classified into conditional or unconditional (i.e., open-ended) approaches depending on the input context provided to the models. This research proposes CodeGenXplainer, a suite of interpretability methods for Unconditional Language Models of source code. CodeGenXplainer comprises four methods leveraging multiple source code features such as embedding representations, code metrics, compilation errors, and token distributions. Additionally, this research presents an empirical study to validate CodeGenXplainer using publicly available data and extensive sampling of code snippets. Furthermore, CodeGenXplainer provides a base conceptual framework that allows studying multiple complementary perspectives based on machine-generated code. Results show that the studied models can generate code exhibiting similar properties to human code, particularly in terms of code metrics, compilation errors, and token-level information; nonetheless, machine-generated code presents issues with the semantic elements of the code. (Texto tomado de la fuente)La generación de código es un problema relevante en ciencias de la computación, que soporta la automatización de tareas como completado de código, síntesis y traducción de programas. En los últimos años, los enfoques de aprendizaje profundo han ganado popularidad en el problema de generación de código y algunos de estos enfoques están basados en modelos de lenguaje. Sin embargo, los estudios existentes se centran principalmente en la evaluación utilizando métricas de aprendizaje automático. Adicionalmente, el proceso de generación se puede clasificar en enfoques condicionales o incondicionales (es decir, open-ended) según el contexto de entrada proporcionado a los modelos. Esta investigación propone CodeGenXplainer, un conjunto de métodos de interpretabilidad para modelos de lenguaje no condicionados de código fuente. CodeGenXplainer comprende cuatro métodos que aprovechan múltiples características de código fuente, como representaciones abstractas, métricas de código, errores de compilación y distribuciones de tokens. Además, esta investigación presenta un estudio empírico para validar CodeGenXplainer utilizando datos disponibles públicamente y muestreo extensivo de fragmentos de código. Por otra parte, CodeGenXplainer proporciona un marco conceptual base que permite estudiar múltiples perspectivas complementarias basadas en código generado por máquina. Los resultados muestran que los modelos estudiados pueden generar código que exhibe propiedades similares al código humano, particularmente en términos de métricas de código, errores de compilación e información a nivel de token; no obstante, el código generado por máquina presenta problemas con los elementos semánticos del código.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónSistemas inteligentesIngeniería de softwarexi, 112 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónComputadoresProcesamiento de la informaciónComputersInformation processingOpen-ended Code GenerationML InterpretabilityLanguage ModelsAutoregressive ModelsNeural NetworksInterpretabilidad de aprendizaje automáticoGeneración No-Condicionada de CódigoModelos de LenguajeModelos AutoregresivosRedes NeuronalesUnderstanding and Implementing Deep Neural Networks for Unconditional Source Code GenerationEntendiendo e implementando redes neuronales profundas para la generación no condicionada de código fuenteTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferencia[1] Karan Aggarwal, Mohammad Salameh, and Abram Hindle. 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In: arXiv (Dec.2020). url: http://arxiv.org/abs/2012.14261Understanding and Implementing Deep Neural Networks for Unconditional Source Code GenerationEstudiantesInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82449/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1019099124.2022.pdf1019099124.2022.pdfTesis de Maestría en Ingeniería de Sistemas y Computaciónapplication/pdf5834584https://repositorio.unal.edu.co/bitstream/unal/82449/2/1019099124.2022.pdfb9131dd0539e6614f7a11c410ec9a6cdMD52THUMBNAIL1019099124.2022.pdf.jpg1019099124.2022.pdf.jpgGenerated Thumbnailimage/jpeg4181https://repositorio.unal.edu.co/bitstream/unal/82449/3/1019099124.2022.pdf.jpgb57c0b6268453cd43c828fc0a80ecbf7MD53unal/82449oai:repositorio.unal.edu.co:unal/824492024-08-12 01:59:13.719Repositorio Institucional Universidad Nacional de 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Understanding and Implementing Deep Neural Networks for Unconditional Source Code Generation

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