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Mining dynamic noteworthy functions in software execution sequences.

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Summary
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This study introduces a new method to identify important functions in software execution by analyzing dynamic function call sequences. The approach effectively mines noteworthy functions, enhancing software quality and security.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Cybersecurity

Background:

  • Software quality is significantly impacted by crucial entities.
  • Existing methods for analyzing software entities, like static analysis, often focus on code structure rather than runtime behavior.
  • Identifying and protecting important software components is vital for development, maintenance, and security.

Purpose of the Study:

  • To propose a novel approach for mining dynamic noteworthy functions (DNFM) from software execution sequences.
  • To enhance software quality and attack-defending capabilities through the identification of critical functions.
  • To provide a method that analyzes software from the perspective of its execution process.

Main Methods:

  • Acquired execution traces by decompiling software and tracking stack changes to obtain function address sequences.
  • Modeled and simplified traces into simplified function sequences (SFS).
  • Employed a pattern extraction (PE) algorithm on SFS and designed inner-importance and inter-importance indicators for function noteworthiness evaluation within the DNFM algorithm.

Main Results:

  • The proposed Dynamic Noteworthy Functions Mining (DNFM) algorithm successfully identified noteworthy functions within software execution.
  • Functions were effectively sorted based on their calculated noteworthiness.
  • Experimental results demonstrated that DNFM outperforms traditional complex network-based methods like PageRank and DegreeRank in mining noteworthy functions.

Conclusions:

  • The DNFM method provides an effective and precise way to mine noteworthy functions by analyzing dynamic execution processes.
  • This approach contributes to improving software quality and enhancing its resilience against attacks.
  • The study highlights the importance of dynamic analysis for understanding and securing software.