{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117815"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117815","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Guiding code analysis and generation with probabilistic constraints","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Wu, Zhengkai"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Xie, Tao","Marinov, Darko","Zhang, Lingming","Singh, Rishabh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Constraint","Program Synthesis","Fuzz Testing","Root Cause Analysis"],"languages":["en","eng"],"rights":["Copyright 2022 Zhengkai Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117815","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Xie, Tao","Marinov, Darko","Zhang, Lingming","Singh, Rishabh"]},{"key":"dc:creator","label":"Author","values":["Wu, Zhengkai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-01"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Constraint","Program Synthesis","Fuzz Testing","Root Cause Analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Zhengkai Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117815"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Zhengkai Wu, accepted the attached license on 2022-12-01 at 12:39.","The student, Zhengkai Wu, submitted this Dissertation for approval on 2022-12-01 at 13:06.","This Dissertation was approved for publication on 2022-12-01 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18716 on 2023-04-12 at 07:36:40","Different forms of constraints have been a useful kind of artifacts across multiple computer science domains, especially in code analysis and generation tasks. Existing constraint-based approaches (being guided by constraints) often work under the assumption that in the solution to the target problem, every constraint must be satisfied. However, we show that this assumption may limit the effectiveness of the existing constraint-based approaches. To overcome the aforementioned limitation, we explore ideas to extract and maintain probabilistic constraints in this dissertation. Probabilistic constraints are defined as constraints assigned with a probability distribution denoting the likelihood of each constraint to be satisfied. The probability distribution allows us to incorporate a broader range of constraints with different levels of confidence reflected by the probability, thus helping with the tasks of code analysis and generation. To effectively guide code analysis and generation with probabilistic constraints, we propose approaches in four specific tasks. In the first half of this dissertation, we investigate the code analysis tasks. In the first part (REINAM), we focus on how to maintain the probability distribution on the grammatical constraints to allow better generalization ability in the grammar-guided fuzzing problem. In the second part (Groot), we focus on how to design different types of constraints to more accurately locate the root cause when conducting root cause analysis in a complicated microservice system. In the second half, we investigate the code generation tasks. In the third part (NL2Viz), we focus on how to extract probabilistic constraints from ambiguous inputs such as natural language (NL) and data context in the task of NL-to-visualization (in short as NL2Visualization). In the fourth part (NRRanker), we focus on how to generate additional probabilistic constraints based on the given NL instruction to rank the initial candidate regular expressions in the task of NL-to-regularexpression (in short as NL2Regex). Based on the empirical evaluations of our proposed approaches in the four tasks, we find that the guidance of probabilistic constraints substantially improves the effectiveness of constraint-based approaches on the tasks of code analysis and generation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Guiding code analysis and generation with probabilistic constraints"]}]}],"canonical_facts":{"dc:contributor":["Xie, Tao","Marinov, Darko","Zhang, Lingming","Singh, Rishabh"],"dc:creator":["Wu, Zhengkai"],"dc:date":["2022-12","2022-12-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Zhengkai Wu, accepted the attached license on 2022-12-01 at 12:39.","The student, Zhengkai Wu, submitted this Dissertation for approval on 2022-12-01 at 13:06.","This Dissertation was approved for publication on 2022-12-01 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18716 on 2023-04-12 at 07:36:40","Different forms of constraints have been a useful kind of artifacts across multiple computer science domains, especially in code analysis and generation tasks. Existing constraint-based approaches (being guided by constraints) often work under the assumption that in the solution to the target problem, every constraint must be satisfied. However, we show that this assumption may limit the effectiveness of the existing constraint-based approaches. To overcome the aforementioned limitation, we explore ideas to extract and maintain probabilistic constraints in this dissertation. Probabilistic constraints are defined as constraints assigned with a probability distribution denoting the likelihood of each constraint to be satisfied. The probability distribution allows us to incorporate a broader range of constraints with different levels of confidence reflected by the probability, thus helping with the tasks of code analysis and generation. To effectively guide code analysis and generation with probabilistic constraints, we propose approaches in four specific tasks. In the first half of this dissertation, we investigate the code analysis tasks. In the first part (REINAM), we focus on how to maintain the probability distribution on the grammatical constraints to allow better generalization ability in the grammar-guided fuzzing problem. In the second part (Groot), we focus on how to design different types of constraints to more accurately locate the root cause when conducting root cause analysis in a complicated microservice system. In the second half, we investigate the code generation tasks. In the third part (NL2Viz), we focus on how to extract probabilistic constraints from ambiguous inputs such as natural language (NL) and data context in the task of NL-to-visualization (in short as NL2Visualization). In the fourth part (NRRanker), we focus on how to generate additional probabilistic constraints based on the given NL instruction to rank the initial candidate regular expressions in the task of NL-to-regularexpression (in short as NL2Regex). Based on the empirical evaluations of our proposed approaches in the four tasks, we find that the guidance of probabilistic constraints substantially improves the effectiveness of constraint-based approaches on the tasks of code analysis and generation."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117815"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Zhengkai Wu"],"dc:subject":["Constraint","Program Synthesis","Fuzz Testing","Root Cause Analysis"],"dc:title":["Guiding code analysis and generation with probabilistic constraints"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}