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Attention based GRU-LSTM for software defect prediction
Software defect prediction (SDP) can be used to produce reliable, high-quality software. The current SDP is practiced on program granular components (such as file level, class level, or function level), which cannot accurately predict failures. To solve this problem, we propose a new framework calle...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7932164/ https://www.ncbi.nlm.nih.gov/pubmed/33661985 http://dx.doi.org/10.1371/journal.pone.0247444 |
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author | Munir, Hafiz Shahbaz Ren, Shengbing Mustafa, Mubashar Siddique, Chaudry Naeem Qayyum, Shazib |
author_facet | Munir, Hafiz Shahbaz Ren, Shengbing Mustafa, Mubashar Siddique, Chaudry Naeem Qayyum, Shazib |
author_sort | Munir, Hafiz Shahbaz |
collection | PubMed |
description | Software defect prediction (SDP) can be used to produce reliable, high-quality software. The current SDP is practiced on program granular components (such as file level, class level, or function level), which cannot accurately predict failures. To solve this problem, we propose a new framework called DP-AGL, which uses attention-based GRU-LSTM for statement-level defect prediction. By using clang to build an abstract syntax tree (AST), we define a set of 32 statement-level metrics. We label each statement, then make a three-dimensional vector and apply it as an automatic learning model, and then use a gated recurrent unit (GRU) with a long short-term memory (LSTM). In addition, the Attention mechanism is used to generate important features and improve accuracy. To verify our experiments, we selected 119,989 C/C++ programs in Code4Bench. The benchmark tests cover various programs and variant sets written by thousands of programmers. As an evaluation standard, compared with the state evaluation method, the recall, precision, accuracy and F1 measurement of our well-trained DP-AGL under normal conditions have increased by 1%, 4%, 5%, and 2% respectively. |
format | Online Article Text |
id | pubmed-7932164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79321642021-03-15 Attention based GRU-LSTM for software defect prediction Munir, Hafiz Shahbaz Ren, Shengbing Mustafa, Mubashar Siddique, Chaudry Naeem Qayyum, Shazib PLoS One Research Article Software defect prediction (SDP) can be used to produce reliable, high-quality software. The current SDP is practiced on program granular components (such as file level, class level, or function level), which cannot accurately predict failures. To solve this problem, we propose a new framework called DP-AGL, which uses attention-based GRU-LSTM for statement-level defect prediction. By using clang to build an abstract syntax tree (AST), we define a set of 32 statement-level metrics. We label each statement, then make a three-dimensional vector and apply it as an automatic learning model, and then use a gated recurrent unit (GRU) with a long short-term memory (LSTM). In addition, the Attention mechanism is used to generate important features and improve accuracy. To verify our experiments, we selected 119,989 C/C++ programs in Code4Bench. The benchmark tests cover various programs and variant sets written by thousands of programmers. As an evaluation standard, compared with the state evaluation method, the recall, precision, accuracy and F1 measurement of our well-trained DP-AGL under normal conditions have increased by 1%, 4%, 5%, and 2% respectively. Public Library of Science 2021-03-04 /pmc/articles/PMC7932164/ /pubmed/33661985 http://dx.doi.org/10.1371/journal.pone.0247444 Text en © 2021 Munir et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Munir, Hafiz Shahbaz Ren, Shengbing Mustafa, Mubashar Siddique, Chaudry Naeem Qayyum, Shazib Attention based GRU-LSTM for software defect prediction |
title | Attention based GRU-LSTM for software defect prediction |
title_full | Attention based GRU-LSTM for software defect prediction |
title_fullStr | Attention based GRU-LSTM for software defect prediction |
title_full_unstemmed | Attention based GRU-LSTM for software defect prediction |
title_short | Attention based GRU-LSTM for software defect prediction |
title_sort | attention based gru-lstm for software defect prediction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7932164/ https://www.ncbi.nlm.nih.gov/pubmed/33661985 http://dx.doi.org/10.1371/journal.pone.0247444 |
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