Scalable Shape Analysis for Systems Code 1st edtion by Hongseok Yang, Oukseh Lee, Josh Berdine, Cristiano Calcagno, Byron Cook, Dino Distefano, Peter O’Hearn ISBN 3540705437 9783540705437

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Product details:

ISBN 10:       3540705437
ISBN 13: 978-3540705437
Author:         Hongseok Yang, Oukseh Lee, Josh Berdine, Cristiano Calcagno, Byron Cook, Dino Distefano, Peter O’Hearn

Pointer safety faults in device drivers are one of the leading causes of crashes in operating systems code. In principle, shape analysis tools can be used to prove the absence of this type of error. In practice, however, shape analysis is not used due to the unacceptable mixture of scalability and precision provided by existing tools. In this paper we report on a new join operation <span id="MathJax-Element-1-Frame" class="MathJax_SVG" style="box-sizing: inherit; display: inline-block; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; font-size-adjust: none; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;" tabindex="0" role="presentation" data-mathml="⊔†”>⊔† for the separation domain which aggressively abstracts information for scalability yet does not lead to false error reports. <span id="MathJax-Element-2-Frame" class="MathJax_SVG" style="box-sizing: inherit; display: inline-block; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; font-size-adjust: none; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;" tabindex="0" role="presentation" data-mathml="⊔†”>⊔† is a critical piece of a new shape analysis tool that provides an acceptable mixture of scalability and precision for industrial application. Experiments on whole Windows and Linux device drivers (firewire, pci-driver, cdrom, md, etc.) represent the first working application of shape analysis to verification of whole industrial programs.

Scalable Shape Analysis for Systems Code 1st Table of contents:

Chapter 1: Introduction
1.1 Overview of Shape Analysis
1.2 Challenges in Analyzing Systems Code
1.3 Scalability Issues in Shape Analysis
1.4 Motivation for Scalable Shape Analysis
1.5 Objectives and Contributions of the Paper
1.6 Structure of the Paper

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Chapter 2: Background and Related Work
2.1 Shape Analysis: Concepts and Techniques
2.2 Classical Shape Analysis Approaches
2.3 Static Analysis for Systems Code
2.4 Prior Work on Scalable Shape Analysis
2.5 Related Techniques in Program Analysis and Verification
2.6 Challenges Addressed by Scalable Shape Analysis

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Chapter 3: Shape Analysis for Systems Code
3.1 Understanding Systems Code and its Characteristics
3.2 Memory Structures in Systems Programming
3.3 Shape Analysis: Identifying Data Structures in Code
3.4 Dynamic Memory Allocation and Deallocation
3.5 Constraints in Systems Code: Complexity and Scalability

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Chapter 4: Principles of Scalable Shape Analysis
4.1 Defining Scalability in Shape Analysis
4.2 Abstraction Techniques for Large-Scale Systems Code
4.3 Efficient Representation of Memory and Data Structures
4.4 Automated Abstraction and Refinement in Shape Analysis
4.5 Trade-offs in Accuracy and Performance

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Chapter 5: Algorithmic Framework for Scalable Shape Analysis
5.1 Overview of the Algorithmic Framework
5.2 Memory Model and Shape Representation
5.3 Scalable Abstraction and Analysis Strategies
5.4 Analysis of Data Structures and Pointer Manipulation
5.5 Algorithmic Complexity and Performance Optimization

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Chapter 6: Tools and Implementation
6.1 Overview of Shape Analysis Tools
6.2 Design and Implementation of the Scalable Shape Analysis Tool
6.3 Integration with Existing Static Analysis Frameworks
6.4 Evaluation of Tool Performance and Scalability
6.5 Case Studies and Example Applications

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Chapter 7: Applications of Scalable Shape Analysis
7.1 Application in Verifying Memory Safety
7.2 Detecting Null Pointer Dereferencing
7.3 Memory Leak Detection in Systems Code
7.4 Automated Refactoring and Code Optimization
7.5 Applications in Real-Time Systems and Embedded Software

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Chapter 8: Challenges and Limitations
8.1 Scalability Challenges in Large-Scale Systems Code
8.2 Handling Complex Data Structures and Pointers
8.3 Precision vs. Efficiency in Shape Analysis
8.4 Dealing with Dynamic Behavior and Non-determinism
8.5 Limitations in Existing Tools and Techniques

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Chapter 9: Open Problems and Future Directions
9.1 Open Problems in Scalable Shape Analysis
9.2 Improving Abstraction Techniques for Complex Systems
9.3 Integration with Dynamic and Hybrid Analysis
9.4 Handling Multi-threaded and Concurrent Code
9.5 Future Research Directions in Shape Analysis for Systems Code

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Chapter 10: Conclusion
10.1 Summary of Contributions
10.2 Impact on Systems Code Analysis and Verification
10.3 Final Thoughts on the Future of Shape Analysis
10.4 Concluding Remarks

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