{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/19301"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/19301","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"Execution Trace Reconstruction Using Diffusion-Based Generative Models","abstract":"Execution tracing is a critical technique for analysing the behaviour of software systems, enabling several key security and performance analysis tasks. However, missing trace events, often resulting from the resource constraints of tracing tools, can compromise the integrity of trace data and impact subsequent analyses. Solutions for trace reconstruction are notably under explored, and consequently the few existing methods frequently fail to fully utilize contextual information leading to poor performance in complex scenarios. This thesis explores the use of diffusion-based generative models, a class of deep learning techniques that have set new benchmarks in various content generation tasks, for reconstructing incomplete trace event sequences. In the first comprehensive evaluation of diffusion models for this purpose, we test their performance using datasets derived from twelve traces collected across four distinct systems. The models are evaluated under various imputation scenarios, including differing sequence lengths and missing data ratios. Among the models tested, the SSSDS4 model demonstrates superior performance, achieving high accuracy, perfect reconstruction rates, and strong ROUGE-L scores across diverse conditions. These findings underscore the potential of diffusion-based models to accurately reconstruct missing events, thereby maintaining trace integrity and enhancing system monitoring and analysis.","abstract_html":"Execution tracing is a critical technique for analysing the behaviour of software systems, enabling several key security and performance analysis tasks. However, missing trace events, often resulting from the resource constraints of tracing tools, can compromise the integrity of trace data and impact subsequent analyses. Solutions for trace reconstruction are notably under explored, and consequently the few existing methods frequently fail to fully utilize contextual information leading to poor performance in complex scenarios. This thesis explores the use of diffusion-based generative models, a class of deep learning techniques that have set new benchmarks in various content generation tasks, for reconstructing incomplete trace event sequences. In the first comprehensive evaluation of diffusion models for this purpose, we test their performance using datasets derived from twelve traces collected across four distinct systems. The models are evaluated under various imputation scenarios, including differing sequence lengths and missing data ratios. Among the models tested, the SSSDS4 model demonstrates superior performance, achieving high accuracy, perfect reconstruction rates, and strong ROUGE-L scores across diverse conditions. These findings underscore the potential of diffusion-based models to accurately reconstruct missing events, thereby maintaining trace integrity and enhancing system monitoring and analysis.","abstract_has_math":false,"creators":["Janecek, Madeline"],"institution":"Brock University","degree_name":"M.Sc. 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Solutions for trace reconstruction are notably under explored, and consequently the few existing methods frequently fail to fully utilize contextual information leading to poor performance in complex scenarios. This thesis explores the use of diffusion-based generative models, a class of deep learning techniques that have set new benchmarks in various content generation tasks, for reconstructing incomplete trace event sequences. In the first comprehensive evaluation of diffusion models for this purpose, we test their performance using datasets derived from twelve traces collected across four distinct systems. The models are evaluated under various imputation scenarios, including differing sequence lengths and missing data ratios. Among the models tested, the SSSDS4 model demonstrates superior performance, achieving high accuracy, perfect reconstruction rates, and strong ROUGE-L scores across diverse conditions. These findings underscore the potential of diffusion-based models to accurately reconstruct missing events, thereby maintaining trace integrity and enhancing system monitoring and analysis."]},{"key":"dc:title","label":"Title","values":["Execution Trace Reconstruction Using Diffusion-Based Generative Models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ezzati-Jivan, Naser"],"dc:contributor.department":["Department of Computer Science"],"dc:creator":["Janecek, Madeline"],"dc:date.accessioned":["2025-05-01T13:44:01Z"],"dc:date.available":["2025-05-01T13:44:01Z"],"dc:date.issued":["2025-05-01T13:44:01Z"],"dc:description.abstract":["Execution tracing is a critical technique for analysing the behaviour of software systems, enabling several key security and performance analysis tasks. However, missing trace events, often resulting from the resource constraints of tracing tools, can compromise the integrity of trace data and impact subsequent analyses. Solutions for trace reconstruction are notably under explored, and consequently the few existing methods frequently fail to fully utilize contextual information leading to poor performance in complex scenarios. This thesis explores the use of diffusion-based generative models, a class of deep learning techniques that have set new benchmarks in various content generation tasks, for reconstructing incomplete trace event sequences. In the first comprehensive evaluation of diffusion models for this purpose, we test their performance using datasets derived from twelve traces collected across four distinct systems. The models are evaluated under various imputation scenarios, including differing sequence lengths and missing data ratios. Among the models tested, the SSSDS4 model demonstrates superior performance, achieving high accuracy, perfect reconstruction rates, and strong ROUGE-L scores across diverse conditions. These findings underscore the potential of diffusion-based models to accurately reconstruct missing events, thereby maintaining trace integrity and enhancing system monitoring and analysis."],"dc:identifier.uri":["https://hdl.handle.net/10464/19301"],"dc:language.iso":["eng"],"dc:publisher":["Brock University"],"dc:subject":["TECHNOLOGY::Information technology::Computer science::Computer science"],"dc:title":["Execution Trace Reconstruction Using Diffusion-Based Generative Models"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Mathematics and Science"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.Sc. Computer Science"]},"updated_at":"2026-07-24T01:23:16Z"}