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Fine-Grained Software Defect Prediction Based on the Method-Call Sequence.

Fengyu Yang1,2, Yaxuan Huang2, Haoming Xu2

  • 1College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

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This study introduces a novel software defect prediction model using method-call sequences and transformer networks. The approach enhances defect prediction accuracy and stability at the method-call sequence level compared to traditional class-level methods.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Current software defect prediction models primarily focus on coarse-grained entities like classes and files.
  • Existing methods struggle to predict a wide range of defects effectively due to limitations in capturing fine-grained code semantics.
  • There is a need to explore inter-method relationships and code structure for improved defect prediction.

Purpose of the Study:

  • To develop a novel defect prediction model that leverages method-call sequences to capture code semantics and syntactic structure.
  • To enhance the granularity of software defect prediction beyond traditional class-level analysis.
  • To improve the accuracy and stability of predicting defect density within software projects.

Main Methods:

  • Generated method-call sequences retaining code context and token sequences for semantic information.
  • Embedded token sequences into method-call sequences and encoded them into fixed-length vectors.
  • Developed a transformer-based defect prediction model to map code vectors to semantic and syntactic features, predicting defect density.

Main Results:

  • The method-call sequence-level prediction demonstrated superior performance compared to class-level prediction.
  • The proposed model achieved more stable prediction results at the method-call sequence level than at the method level.
  • The approach resulted in an 8% lower mean absolute error (MAE) compared to other deep learning methods.

Conclusions:

  • Method-call sequence-level defect prediction offers significant advantages in accuracy and stability over class-level approaches.
  • The transformer-based model effectively learns code semantics and syntactic structures for precise defect prediction.
  • This research advances software defect prediction by enabling finer-grained analysis and improved defect detection capabilities.