Powering reasoning about complex software systems through heuristic methods

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dc.contributor.advisorKhurshid, Sarfraz
dc.contributor.committeeMemberGligoric, Milos
dc.contributor.committeeMemberPasareanu, Corina
dc.contributor.committeeMemberJulien, Christine
dc.contributor.committeeMemberNikolova, Evdokia
dc.creatorConverse, Hayes Elliott
dc.creator.orcid0000-0002-0930-2473
dc.date.accessioned2021-05-12T03:38:00Z
dc.date.available2021-05-12T03:38:00Z
dc.date.created2020-05
dc.date.issued2020-05-05
dc.date.submittedMay 2020
dc.date.updated2021-05-12T03:38:00Z
dc.description.abstractToday's real-world software systems are often too complex to reason about formally, which can cause expensive failures which could be avoided with improved analysis in the process of their creation. We here seek to demonstrate that heuristic methods can improve the techniques used to enable and enhance explainability and reasoning for these systems, such as symbolic execution and model checking, thus making the systems they support easier to design, develop, and debug. To this end, we propose a set of new tools for a diverse set of traditionally difficult-to-analyze systems, including neural networks and symbolic execution engines. These tools and techniques use approximation-based insights to show the power of this idea. Experimental evaluation shows that these techniques and tools can improve both explanability and analyzability.
dc.description.departmentElectrical and Computer Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/85647
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/12598
dc.language.isoen
dc.subjectSoftware testing
dc.subjectSoftware verification
dc.subjectNeural networks
dc.subjectHeuristics
dc.subjectSymbolic execution
dc.subjectProgram exploration
dc.titlePowering reasoning about complex software systems through heuristic methods
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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