Effective Instrumentation and Runtime Support for Enhancing Software Reliability
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| Vydáno v: | PQDT - Global (2025) |
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ProQuest Dissertations & Theses
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| On-line přístup: | Citation/Abstract Full Text - PDF Full text outside of ProQuest |
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| 001 | 3261531645 | ||
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Ling, Hao | |
| 245 | 1 | |a Effective Instrumentation and Runtime Support for Enhancing Software Reliability | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a Modern system and software development have evolved far beyond the traditional “code, compile, and deploy” paradigm. Dynamic analysis and runtime monitoring have become essential for enhancing testing, debugging, verification, and optimization in continuous development workflows. However, standard compilation processes and runtime environments often fail to capture the diverse data required for effective dynamic analysis. Integrating additional compilation and runtime support addresses this gap, but often incurs significant overhead, limiting the scalability and applicability of the techniques. This thesis contributes to improving the scalability and practicality of dynamic analysis at an industrial scale. We present three key contributions that advance instrumentation (i.e., the automatic insertion of analysis instructions) and the associated runtime support to enhance dynamic analysis throughout the entire development lifecycle. First, GiantSan introduces innovative memory continuity-based instrumentation for enhancing the efficiency of memory protections. GiantSan significantly improves the accuracy and effectiveness of memory sanitizers, which are critical tools for identifying memory-related vulnerabilities. GiantSan achieves performance gains by utilizing a novel and efficient interval validation algorithm, called Segment Folding, and complementing traditional machine instruction-level protections with valuable language-level information.Second, Spinel addresses the challenge of memory-related event monitoring in software testing. Existing methods lack efficient mechanisms for extracting memory-related guidance in automatic testing. Spinel presents a lightweight runtime framework with spatial encoding, capturing essential insights with minimal overhead while cooperating with offline analysis to efficiently expose hidden bugs. Third, Zircon provides a compiler-friendly solution to the optimization failure issue of event quantification. By refining data flow through specialized static analyses and tailored compilation passes, Zircon ensures the quality of code optimization with comprehensive instrumentation. These contributions collectively pave the way for more efficient, accurate, and scalable methods tailored for contemporary software systems. Notably, our research prototypes have been successfully implemented within a Fortune 500 company, demonstrating their potential for industrial-scale application. | |
| 653 | |a Computer science | ||
| 653 | |a Computer engineering | ||
| 773 | 0 | |t PQDT - Global |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3261531645/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3261531645/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://doi.org/10.14711/thesis-hdl152469 |