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CVDF DYNAMIC-A Dynamic Fuzzy Testing Sample Generation Framework Based on BI-LSTM and Genetic Algorithm.

Mingrui Ma1, Lansheng Han1, Yekui Qian2

  • 1School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

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|February 15, 2022
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Summary
This summary is machine-generated.

This study introduces CVDF DYNAMIC, a novel framework for generating effective fuzzy testing samples. It enhances vulnerability mining by combining code coverage and deep path detection capabilities.

Keywords:
Bi-LSTM neural networkdeep learningfuzzy testing sample generationgenetic algorithm

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Fuzzy testing is crucial for vulnerability mining, but sample generation methods often struggle to balance code coverage and complex path detection.
  • Existing techniques focus on limited aspects, hindering comprehensive vulnerability discovery.

Purpose of the Study:

  • To propose CVDF DYNAMIC, an ensemble learning-based framework for generating fuzzy testing samples with enhanced code coverage and path depth detection.
  • To improve the efficiency and effectiveness of vulnerability mining in software.

Main Methods:

  • Utilizing a Bidirectional Long Short-Term Memory (BI-LSTM) neural network and a genetic algorithm for test case generation.
  • Integrating generated sample sets using ensemble learning principles.
  • Employing a heuristic genetic algorithm for sample set simplification.
  • Introducing a novel evaluation index for path depth detection ability (pdda).

Main Results:

  • CVDF DYNAMIC successfully generates sample sets with both high code coverage and significant path depth detection.
  • The proposed pdda index effectively measures the vulnerability mining capability of the generated test cases.
  • Comparative analysis shows CVDF DYNAMIC outperforms existing fuzzy testing tools and methods.

Conclusions:

  • CVDF DYNAMIC offers a robust approach to fuzzy testing sample generation, improving vulnerability discovery.
  • The framework's ensemble learning strategy and novel evaluation index represent significant advancements in the field.
  • Future work will focus on further optimizing CVDF DYNAMIC's efficiency and applicability.