An Efficient Technique for Dynamic Slicing of Concurrent Java Programs 1st Edition by D P Mohapatra, Rajib Mall, Rajeev Kumar ISBN 9783540301769

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ISBN 10: 
ISBN 13: 9783540301769
Author: D P Mohapatra, Rajib Mall, Rajeev Kumar

Program slice has many applications such as program debugging, testing, maintenance and complexity measurement. We propose a new dynamic program slicing technique for concurrent Java programs that is more efficient than the related algorithms. We introduce the notion of Concurrent Program Dependence Graph (CPDG). Our algorithm uses CPDG as the intermediate representation and is based on marking and unmarking the edges in the CPDG as and when the dependencies arise and cease during run-time. Our approach eliminates the use of trace files and is more efficient than the existing algorithms.

An Efficient Technique for Dynamic Slicing of Concurrent Java Programs 1st Table of contents:

• Background and Related Work

  • 2.1 Program Slicing: Concepts and Techniques
  • 2.2 Types of Program Slicing: Static vs. Dynamic Slicing
  • 2.3 Challenges in Dynamic Slicing of Concurrent Programs
  • 2.4 Existing Techniques for Slicing Java Programs
  • 2.5 Related Work on Slicing in Concurrent Programming Models

• Concurrency in Java Programs

  • 3.1 Introduction to Java Concurrency
  • 3.2 Threads, Locks, and Synchronization in Java
  • 3.3 Shared Resources and Race Conditions
  • 3.4 Concurrent Program Execution Models in Java
  • 3.5 Impact of Concurrency on Program Analysis

• Dynamic Program Slicing

  • 4.1 Definition and Overview of Dynamic Slicing
  • 4.2 Difference Between Static and Dynamic Slicing
  • 4.3 Techniques for Collecting Execution Traces
  • 4.4 Challenges in Dynamic Slicing for Concurrent Programs
  • 4.5 Benefits of Dynamic Slicing in Debugging and Program Understanding

• An Efficient Technique for Dynamic Slicing of Concurrent Java Programs

  • 5.1 Key Ideas and Assumptions Behind the Proposed Technique
  • 5.2 Handling Thread Interleavings and Synchronization in Dynamic Slicing
  • 5.3 Tracking Shared Variables and Memory Locations
  • 5.4 Optimization Strategies for Efficient Slicing
  • 5.5 Algorithm for Dynamic Slicing of Concurrent Java Programs
  • 5.6 Data Structures and Techniques for Efficient Trace Collection

• Implementation of the Dynamic Slicing Technique

  • 6.1 Overview of the Implementation Framework
  • 6.2 Tool Design and Architecture
  • 6.3 Integration with Java Development Environments
  • 6.4 Handling Real-World Concurrent Java Applications
  • 6.5 Code Example and Demonstration of the Technique

• Experimental Evaluation

  • 7.1 Evaluation Criteria and Metrics
  • 7.2 Benchmark Programs and Test Cases
  • 7.3 Comparison with Existing Techniques
  • 7.4 Performance Evaluation: Time and Space Complexity
  • 7.5 Case Studies: Analyzing Concurrent Java Programs with the Technique

• Challenges and Limitations

  • 8.1 Handling Complex Synchronization Patterns
  • 8.2 Scalability Issues in Large-Scale Concurrent Programs
  • 8.3 Trade-Offs Between Precision and Efficiency
  • 8.4 Managing Non-Determinism in Concurrent Execution
  • 8.5 Limitations of the Current Approach and Future Improvements

• Applications of Dynamic Slicing in Debugging and Optimization

  • 9.1 Using Dynamic Slicing for Debugging Concurrent Java Programs
  • 9.2 Performance Tuning and Optimization with Program Slicing
  • 9.3 Identifying Bottlenecks and Deadlocks in Concurrent Programs
  • 9.4 Enhancing Code Understanding and Maintenance
  • 9.5 Integration with Other Program Analysis Techniques

• Future Directions and Research Opportunities

  • 10.1 Enhancing the Technique for Multi-Core and Distributed Systems
  • 10.2 Integration of Dynamic Slicing with Static Analysis Tools
  • 10.3 Extending the Approach to Other Programming Languages
  • 10.4 Machine Learning Techniques for Improving Slicing Efficiency
  • 10.5 Research on Handling Non-Deterministic Behavior in Concurrency

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