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Automatic software code repair using deep learning techniques

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Abstract

In the multi-hundred-billion-dollar industry of software development, the debugging process is an expensive task for developers. So, much effort has been put into debugging automation. In the last decade, researchers have repaired codes according to predefined rules which are only effective in limited types of bugs. Through a lot of experiments, researchers have found that deep learning models are useful in code repair automation similar to the revolutionary results these approaches have produced in various other fields. To solve such a difficult problem, recent works focus on debugging bugs that appear on one line of code. It has been reported that this type of bug occurs at an average rate of 1 out of every 1600 lines of code in a software project, which is significant. The current research follows these approaches and introduces a novel automatic code-repair system. We have employed the transfer learning technique to reuse a pre-trained model on the problem. The proposed system is designed based on the encoder-decoder architecture. In the encoder, a new pre-trained Bert model named JavaBert is used. Then, the model was fine tuned. The decoder is a transformer with an autoregressive structure. ManySStuBs4J [1] dataset is used for evaluation purposes. The results of the evaluations show that the proposed system has higher accuracy and BLEU criteria than CodeBert and the baseline model. Baseline is a simple model that acts as a reference in machine learning studies and CodeBert is one of the most similar models to the proposed model. The bilingual evaluation understudy score (BLEU) improvement is between 0.04 and 0.16%, the accuracy improvement is between 0.64 and 5.81%, the recall improvement is between 1.08 and 9.2%, and the F-score improvement is between 3.27 and 6.18%.

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Data availability

To evaluate the proposed system, the ManySStuBs4J (Karampatsis & Sutton, 2020) dataset is used. No new datasets were created in this study.

Notes

  1. In computer-based language recognition, ANTLR, or ANother Tool for Language Recognition, is a parser generator that uses an LL algorithm for parsing.

  2. https://github.com/GumTreeDiff/gumtree

Abbreviations

BLEU:

Bilingual Evaluation Understudy Score

GRU:

Gated Recurrent Unit

LSTM:

Long Short-Term Memory Networks

AST:

Abstract Syntax Tree

CNN:

Convolutional Neural Network

BERT:

Bidirectional Encoder Representations from Transformers

MLP:

A multilayer perceptron

DNN:

Deep Neural Networks

RNN:

Recurrent neural network

Seq2Seq:

Sequence-to-Sequence

BPE:

Byte Pair Encoding

API:

Application Programming Interface

GPU:

Graphics processing unit

MLM:

Masked Language Model

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Authors and Affiliations

  1. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Mohammad Mahdi Abdollahpour, Mehrdad Ashtiani & Fatemeh Bakhshi

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  1. Mohammad Mahdi Abdollahpour
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  2. Mehrdad Ashtiani
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Contributions

Mohammad Mahdi Abdollahpour: Conceptualization, Methodology, Software, Visualization and Original draft preparation. Mehrdad Ashtiani: Conceptualization, Writing- Reviewing and Editing, Verification of the results, Supervision. Fatemeh Bakhshi: Writing- Reviewing and Editing, Verification of the results.

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Correspondence to Mehrdad Ashtiani.

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Abdollahpour, M.M., Ashtiani, M. & Bakhshi, F. Automatic software code repair using deep learning techniques. Software Qual J 32, 361–390 (2024). https://doi.org/10.1007/s11219-023-09653-1

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