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Related Experiment Video

Updated: Nov 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Attention based GRU-LSTM for software defect prediction.

Hafiz Shahbaz Munir1, Shengbing Ren1, Mubashar Mustafa1

  • 1Computer Science and Engineering, Central South University, Changsha, China.

Plos One
|March 4, 2021
PubMed
Summary

This study introduces DP-AGL, a novel framework for statement-level software defect prediction (SDP). DP-AGL utilizes attention-based GRU-LSTM to significantly improve the accuracy of identifying software failures.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Current software defect prediction (SDP) methods operate at the file, class, or function level, limiting prediction accuracy.
  • Accurate defect prediction is crucial for developing reliable, high-quality software.

Purpose of the Study:

  • To propose a novel framework, DP-AGL, for statement-level defect prediction.
  • To enhance the accuracy of software defect prediction by moving from component-level to statement-level analysis.

Main Methods:

  • Developed a framework (DP-AGL) employing an attention-based GRU-LSTM model for statement-level defect prediction.
  • Utilized clang to construct Abstract Syntax Trees (ASTs) and defined 32 statement-level metrics.
  • Engineered a three-dimensional vector representation for labeled statements, feeding into the GRU-LSTM model with an Attention mechanism.

Main Results:

  • The DP-AGL framework demonstrated improved performance metrics compared to existing methods.
  • Recall, precision, accuracy, and F1-score saw increases of 1%, 4%, 5%, and 2%, respectively.
  • Experiments were validated on a large dataset of 119,989 C/C++ programs from Code4Bench.

Conclusions:

  • Statement-level defect prediction using DP-AGL offers a more accurate approach to identifying software failures.
  • The proposed framework, leveraging attention-based GRU-LSTM and AST metrics, enhances software quality assurance.
  • DP-AGL shows significant potential for improving the reliability of software development processes.