[Eecs_bsee] Canceled: EECS Research Seminar Talk-Towards Practical Software Quality Assurance Techniques via Automated Support of the Development and Usage Process

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2/28/2024 update-this is being cancelled.

Title:
Towards Practical Software Quality Assurance Techniques via
Automated Support of the Development and Usage Process
Bio:
Austin Mordahl is a final-year Ph.D. candidate at the University of Texas at Dallas. His research
is in the area of Software Engineering. Specifically, he focuses on advancing the state-of-the-art
in automated software quality assurance techniques (e.g., static program analysis and fuzz
testing) through various techniques such as machine learning, and software testing and
debugging. He regularly publishes at the top-tier software engineering conferences and
journals, such as ICSE, FSE, ASE, ISSTA, and EMSE. He was a recipient of the prestigious
National Science Foundation (NSF) Graduate Research Fellowship, and the Eugene McDermott
Graduate Research Fellowship in 2020. Moreover, he won the ACM Student Research
Competition at ICSE 2019. More information is available at https://austinmordahl.com<https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Faustinmordahl.com%2F&data=05%7C02%7Ceecs_bsee%40listserv.ohio.edu%7C6a6cf2ea4f114b6c0bf208dc388baac2%7Cf3308007477c4a70888934611817c55a%7C0%7C0%7C638447419740028724%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=cqqU2RNsKIGr9Uz06PvHlRfnRu2TgENhcGIJF5BKWdU%3D&reserved=0 
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Abstract:
Automated software quality assurance approaches are an important guardrail against the
proliferation of bugs and security issues in software. However, challenges like unreliable results,
difficulty in configuration, and high false positive rates prevent many of these techniques from
seeing widespread adoption. In this talk, I will present my work aimed at breaking these barriers
to the usage of automated software quality assurance techniques, with a specific focus on static
analysis. My work improves automated software quality assurance techniques along various
dimensions throughout their development and usage processes; specifically, with regard to their
reliability, usability, and applicability. First, I will share my experience improving the reliability of
static analysis tools through the development of an automated testing and debugging
framework, enabled by a novel theoretical model of static analysis tools' configuration spaces.
This framework has allowed the detection of dozens of bugs in popular static analysis tools.
Second, I will detail the work that aims to improve usability by adapting machine learning
models to automate tedious, manual tasks in the usage process of static analysis tools,
including configuring these tools for specific target programs and classifying false positives.
Finally, I will provide my vision for the future of automatic software quality assurance techniques,
which involves the improvement of the development and usage processes through automated,
adaptive, and explainable techniques that provide a seamless developer and user experience. I
will touch on three concrete research projects I plan to undertake in the next 5 years towards
this vision.
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