Structuring meaningful bug‐fixing patches to fix software defect
Abstract Currently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and res...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-08-01
|
| Series: | IET Software |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sfw2.12140 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849764668045787136 |
|---|---|
| author | Hui Li Yong Liu Xuexin Qi Xi Yu Shikai Guo |
| author_facet | Hui Li Yong Liu Xuexin Qi Xi Yu Shikai Guo |
| author_sort | Hui Li |
| collection | DOAJ |
| description | Abstract Currently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and resources. To address this challenge, researchers developed various solutions utilising deep neural networks. However, these solutions are frequently challenged by issues, such as a vast vocabulary, network training difficulties and elongated training processes resulting from the handling of redundant information. To overcome these limitations, the authors proposed a new neural network‐based model named HopFix, designed to detect software defects that may be introduced during the coding process. HopFix consists of four parts: data preprocessing, encoder, decoder and code generation components, which were used for preprocessing data, extracting information about software defects, analysing defect information, generating software patches and controlling the generation process of software patches, respectively. Experimental studies on Bug‐Fix Pairs (BFP) show that HopFix correctly fixed 47.2% (BFPsmall datasets) and 25.7% (BFPmedium datasets) of software defects. |
| format | Article |
| id | doaj-art-c1e5eeb150f94741a7a011b6cf323b75 |
| institution | DOAJ |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-c1e5eeb150f94741a7a011b6cf323b752025-08-20T03:05:04ZengWileyIET Software1751-88061751-88142023-08-0117456658110.1049/sfw2.12140Structuring meaningful bug‐fixing patches to fix software defectHui Li0Yong Liu1Xuexin Qi2Xi Yu3Shikai Guo4The College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaAbstract Currently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and resources. To address this challenge, researchers developed various solutions utilising deep neural networks. However, these solutions are frequently challenged by issues, such as a vast vocabulary, network training difficulties and elongated training processes resulting from the handling of redundant information. To overcome these limitations, the authors proposed a new neural network‐based model named HopFix, designed to detect software defects that may be introduced during the coding process. HopFix consists of four parts: data preprocessing, encoder, decoder and code generation components, which were used for preprocessing data, extracting information about software defects, analysing defect information, generating software patches and controlling the generation process of software patches, respectively. Experimental studies on Bug‐Fix Pairs (BFP) show that HopFix correctly fixed 47.2% (BFPsmall datasets) and 25.7% (BFPmedium datasets) of software defects.https://doi.org/10.1049/sfw2.12140crowdsourcingsoftware engineeringsoftware maintenance |
| spellingShingle | Hui Li Yong Liu Xuexin Qi Xi Yu Shikai Guo Structuring meaningful bug‐fixing patches to fix software defect IET Software crowdsourcing software engineering software maintenance |
| title | Structuring meaningful bug‐fixing patches to fix software defect |
| title_full | Structuring meaningful bug‐fixing patches to fix software defect |
| title_fullStr | Structuring meaningful bug‐fixing patches to fix software defect |
| title_full_unstemmed | Structuring meaningful bug‐fixing patches to fix software defect |
| title_short | Structuring meaningful bug‐fixing patches to fix software defect |
| title_sort | structuring meaningful bug fixing patches to fix software defect |
| topic | crowdsourcing software engineering software maintenance |
| url | https://doi.org/10.1049/sfw2.12140 |
| work_keys_str_mv | AT huili structuringmeaningfulbugfixingpatchestofixsoftwaredefect AT yongliu structuringmeaningfulbugfixingpatchestofixsoftwaredefect AT xuexinqi structuringmeaningfulbugfixingpatchestofixsoftwaredefect AT xiyu structuringmeaningfulbugfixingpatchestofixsoftwaredefect AT shikaiguo structuringmeaningfulbugfixingpatchestofixsoftwaredefect |