Software defect prediction via LSTM
Software quality plays an important role in the software lifecycle. Traditional software defect prediction approaches mainly focused on using hand-crafted features to detect defects. However, like human languages, programming languages contain rich semantic and structural information, and the cause...
Uložené v:
| Vydané v: | IET software Ročník 14; číslo 4; s. 443 - 450 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
The Institution of Engineering and Technology
01.08.2020
|
| Predmet: | |
| ISSN: | 1751-8806, 1751-8814, 1751-8814 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Software quality plays an important role in the software lifecycle. Traditional software defect prediction approaches mainly focused on using hand-crafted features to detect defects. However, like human languages, programming languages contain rich semantic and structural information, and the cause of defective code is closely related to its context. Failing to catch this significant information, the performance of traditional approaches is far from satisfactory. In this study, the authors leveraged a long short-term memory (LSTM) network to automatically learn the semantic and contextual features from the source code. Specifically, they first extract the program's Abstract Syntax Trees (ASTs), which is made up of AST nodes, and then evaluate what and how much information they can preserve for several node types. They traverse the AST of each file and fed them into the LSTM network to automatically the semantic and contextual features of the program, which is then used to determine whether the file is defective. Experimental results on several opensource projects showed that the proposed LSTM method is superior to the state-of-the-art methods. |
|---|---|
| AbstractList | Software quality plays an important role in the software lifecycle. Traditional software defect prediction approaches mainly focused on using hand‐crafted features to detect defects. However, like human languages, programming languages contain rich semantic and structural information, and the cause of defective code is closely related to its context. Failing to catch this significant information, the performance of traditional approaches is far from satisfactory. In this study, the authors leveraged a long short‐term memory (LSTM) network to automatically learn the semantic and contextual features from the source code. Specifically, they first extract the program's Abstract Syntax Trees (ASTs), which is made up of AST nodes, and then evaluate what and how much information they can preserve for several node types. They traverse the AST of each file and fed them into the LSTM network to automatically the semantic and contextual features of the program, which is then used to determine whether the file is defective. Experimental results on several opensource projects showed that the proposed LSTM method is superior to the state‐of‐the‐art methods. Software quality plays an important role in the software lifecycle. Traditional software defect prediction approaches mainly focused on using hand‐crafted features to detect defects. However, like human languages, programming languages contain rich semantic and structural information, and the cause of defective code is closely related to its context. Failing to catch this significant information, the performance of traditional approaches is far from satisfactory. In this study, the authors leveraged a long short‐term memory (LSTM) network to automatically learn the semantic and contextual features from the source code. Specifically, they first extract the program's Syntax Trees (ASTs), which is made up of AST nodes, and then evaluate what and how much information they can preserve for several node types. They traverse the AST of each file and fed them into the LSTM network to automatically the semantic and contextual features of the program, which is then used to determine whether the file is defective. Experimental results on several opensource projects showed that the proposed LSTM method is superior to the state‐of‐the‐art methods. |
| Author | Qiu, Shaojian Lu, Lu Deng, Jiehan |
| Author_xml | – sequence: 1 givenname: Jiehan surname: Deng fullname: Deng, Jiehan organization: School of Computer Science and Engineering, South China University of Technology, GuangZhou, People's Republic of China – sequence: 2 givenname: Lu surname: Lu fullname: Lu, Lu email: lul@scut.edu.cn organization: School of Computer Science and Engineering, South China University of Technology, GuangZhou, People's Republic of China – sequence: 3 givenname: Shaojian surname: Qiu fullname: Qiu, Shaojian organization: School of Computer Science and Engineering, South China University of Technology, GuangZhou, People's Republic of China |
| BookMark | eNqFkMFOwzAMQCM0JLbBB3CrxLnFSZql5QYTBaQhDhviGKWpI2Ua7ZQUpv39Ug1x4DBOtmQ_288TMmq7Fgm5ppBRyMtbh30asM0Y0DIDmpdnZEyloGlR0Hz0m8PsgkxCWAMIIXg5JjfLzvY77TFp0KLpk63HxpnedW3y7XSyWK5eL8m51ZuAVz9xSt6rx9X8OV28Pb3M7xep4VKKVMwk4w3TDRU5t6IuDC-MRhSNNJyjlYVkTNRW17GalxoATK21YfFEzijyKZHHucZ3IXi0yrheD6f0XruNoqAGVxVdVXRVg6saXCNJ_5Bb7z61359k7o7Mzm1w_z-gltUHe6gAWCkinB7hoW3dffk2PubEsgNO5ntm |
| CitedBy_id | crossref_primary_10_1007_s10115_025_02534_y crossref_primary_10_1109_TR_2024_3354965 crossref_primary_10_32604_cmc_2024_058931 crossref_primary_10_1155_2022_4311548 crossref_primary_10_32604_cmc_2023_043680 crossref_primary_10_1007_s00500_022_06830_5 crossref_primary_10_1007_s10515_022_00344_y crossref_primary_10_3390_app11114793 crossref_primary_10_1007_s42979_024_03458_0 crossref_primary_10_1007_s10844_023_00793_1 crossref_primary_10_1109_ACCESS_2024_3362896 crossref_primary_10_3390_math10173120 crossref_primary_10_1049_2024_5102699 crossref_primary_10_1007_s11432_023_4127_5 crossref_primary_10_1038_s41598_024_65639_4 crossref_primary_10_1177_18724981241307452 crossref_primary_10_1016_j_jss_2022_111537 crossref_primary_10_1109_ACCESS_2024_3409709 crossref_primary_10_1007_s11042_022_14065_7 crossref_primary_10_1109_TR_2022_3165115 crossref_primary_10_1145_3643727 crossref_primary_10_1016_j_infsof_2021_106588 crossref_primary_10_1049_2024_5550801 crossref_primary_10_1007_s10586_023_04170_z crossref_primary_10_1007_s11334_023_00542_1 crossref_primary_10_1007_s11334_025_00601_9 crossref_primary_10_1007_s10664_023_10371_2 crossref_primary_10_1016_j_compbiomed_2025_110130 crossref_primary_10_32604_cmc_2024_057697 crossref_primary_10_1007_s00521_024_10937_1 crossref_primary_10_1371_journal_pone_0307112 crossref_primary_10_1002_smr_2402 crossref_primary_10_1007_s11219_024_09704_1 crossref_primary_10_1016_j_cose_2024_104024 crossref_primary_10_7717_peerj_cs_739 crossref_primary_10_1049_2024_8027037 crossref_primary_10_3390_e24101373 |
| Cites_doi | 10.1109/TSE.1976.233837 10.1109/ASRU.2013.6707742 10.1162/neco.1997.9.8.1735 10.1109/IESYS.2017.8233558 10.1007/978-3-319-25159-2_49 10.1049/iet-sen.2017.0148 10.1109/IRI.2015.76 10.1049/cp.2013.2313 10.1109/32.295895 10.1109/72.279181 10.1109/TSE.2016.2597849 10.1142/S0218001419590377 10.1145/1868328.1868342 10.21437/Interspeech.2010-343 10.1109/ESEM.2013.20 10.1109/ISCID.2018.00023 10.1145/2884781.2884804 10.1109/QRS.2017.42 10.3233/IDA-150789 10.21437/Interspeech.2012-65 10.1109/TMM.2017.2729019 10.1109/ICSE.2012.6227135 10.1162/evco.2009.17.3.275 10.1016/j.infsof.2015.01.014 10.4018/IJOSSP.2018010101 10.3115/v1/D14-1179 10.1109/ICSE.2015.139 |
| ContentType | Journal Article |
| Copyright | The Institution of Engineering and Technology 2020 The Institution of Engineering and Technology |
| Copyright_xml | – notice: The Institution of Engineering and Technology – notice: 2020 The Institution of Engineering and Technology |
| DBID | AAYXX CITATION |
| DOI | 10.1049/iet-sen.2019.0149 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1751-8814 |
| EndPage | 450 |
| ExternalDocumentID | 10_1049_iet_sen_2019_0149 SFW2BF00295 |
| Genre | article |
| GrantInformation_xml | – fundername: National Nature Science Foundation of China grantid: 61370103 – fundername: the Guangzhou Produce & Research Fund grantid: 201902020004 – fundername: Guangdong Province Application Major Fund grantid: 2015B010131013 – fundername: National Nature Science Foundation of China funderid: 61370103 – fundername: Guangdong Province Application Major Fund funderid: 2015B010131013 – fundername: Guangzhou Produce & Research Fund funderid: 201902020004 |
| GroupedDBID | 0R 24P 29I 3V. 4.4 4IJ 5GY 6IK 8AL 8FE 8FG 8VB AAJGR ABJCF ABPTK ABUWG ACDCL ACGFS ACIWK AENEX AFKRA ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BENPR BFFAM BGLVJ BPHCQ CS3 DU5 DWQXO EBS EJD ESX GNUQQ GOZPB GRPMH HCIFZ HZ IFIPE IPLJI JAVBF K6V K7- L6V LAI LOTEE LXI M0N M43 M7S MS NADUK NXXTH O9- OCL P62 PQEST PQQKQ PQUKI PROAC PTHSS QWB RIE RNS RUI U5U UNMZH UNR ZL0 .DC 0R~ 0ZK 1OC 2QL 96U AAHJG AAMMB ABMDY ABQXS ACCMX ACESK ACGFO ACXQS ADEYR AEFGJ AEGXH AFAZI AGXDD AIDQK AIDYY ALUQN AVUZU CCPQU F8P GROUPED_DOAJ HZ~ IAO IDLOA ITC K1G MCNEO MS~ OK1 PHGZM PHGZT PQGLB PUEGO WIN AAYXX AFFHD CITATION |
| ID | FETCH-LOGICAL-c3775-56723d2ad1543f5b8c38caee5d7c33ef787225bfab3f549a000cbaac2881321e3 |
| IEDL.DBID | 24P |
| ISICitedReferencesCount | 46 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000588424400012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1751-8806 1751-8814 |
| IngestDate | Wed Oct 29 21:16:22 EDT 2025 Tue Nov 18 22:44:06 EST 2025 Tue Sep 09 05:10:04 EDT 2025 Tue Jan 05 21:48:47 EST 2021 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | program debugging open source projects long short-term memory network contextual features LSTM defective code public domain software program diagnostics AST node sequence human languages recurrent neural nets trees (mathematics) software lifecycle software defect prediction approaches software quality machine learning techniques programming languages word embedding techniques program abstract syntax trees feature extraction structural information learning (artificial intelligence) semantic features numerical vectors |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3775-56723d2ad1543f5b8c38caee5d7c33ef787225bfab3f549a000cbaac2881321e3 |
| OpenAccessLink | https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-sen.2019.0149 |
| PageCount | 8 |
| ParticipantIDs | iet_journals_10_1049_iet_sen_2019_0149 crossref_primary_10_1049_iet_sen_2019_0149 wiley_primary_10_1049_iet_sen_2019_0149_SFW2BF00295 crossref_citationtrail_10_1049_iet_sen_2019_0149 |
| ProviderPackageCode | RUI |
| PublicationCentury | 2000 |
| PublicationDate | 20200800 August 2020 2020-08-00 |
| PublicationDateYYYYMMDD | 2020-08-01 |
| PublicationDate_xml | – month: 8 year: 2020 text: 20200800 |
| PublicationDecade | 2020 |
| PublicationTitle | IET software |
| PublicationYear | 2020 |
| Publisher | The Institution of Engineering and Technology |
| Publisher_xml | – name: The Institution of Engineering and Technology |
| References | Chidamber, S.R.; Kemerer, C.F. (C7) 1994; 20 Hochreiter, S.; Schmidhuber, J. (C22) 1997; 9 Chen, L.; Fang, B.; Shang, Z. (C29) 2015; 62 Jing, X.-Y.; Wu, F.; Dong, X. (C30) 2017; 43 García, S.; Herrera, F. (C32) 2014; 17 Su, C.; Ju, S.; Liu, Y. (C34) 2015; 19 Gao, L.; Guo, Z.; Zhang, H. (C17) 2017; 19 Wang, S.; Liu, T.; Nam, J. (C20) 2018 McCabe, T.J. (C6) 1976; SE-2 Li, Z.; Jing, X.-Y.; Zhu, X. (C18) 2018; 12 Qiu, S.; Lu, L.; Jiang, S. (C4) 2019; 33 Bengio, Y.; Simard, P.; Frasconi, P. (C21) 1994; 5 Kakkar, M.; Jain, S.; Bansal, A. (C1) 2018; 9 2018; 9 2015; 19 2012 2011 2019; 33 2010 2015; 62 2017; 43 1976 2019 2018 2017 2016 2017; 19 2015 2014 2013 2014; 17 2018; 12 1994; 5 1997; 9 1977 1994; 20 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_34_1 Han J. (e_1_2_9_36_1) 2011 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_14_1 e_1_2_9_17_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_24_1 e_1_2_9_23_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 Wang S. (e_1_2_9_21_1) 2018 e_1_2_9_9_1 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_27_1 e_1_2_9_29_1 |
| References_xml | – volume: 9 start-page: 1 issue: 1 year: 2018 end-page: 19 ident: C1 article-title: Combining data preprocessing methods with imputation techniques for software defect prediction publication-title: Int. J. Open Source Softw. Processes (IJOSSP) – volume: 12 start-page: 161 issue: 3 year: 2018 end-page: 175 ident: C18 article-title: Progress on approaches to software defect prediction publication-title: IET Softw. – volume: 19 start-page: 1409 issue: 6 year: 2015 end-page: 1432 ident: C34 article-title: Improving random forest and rotation forest for highly imbalanced datasets publication-title: Intell. Data Anal. – volume: 20 start-page: 476 issue: 6 year: 1994 end-page: 493 ident: C7 article-title: A metrics suite for object oriented design publication-title: IEEE Trans. Softw. Eng. – volume: 33 start-page: 1959037 issue: 12 year: 2019 ident: C4 article-title: An investigation of imbalanced ensemble learning methods for cross-project defect prediction publication-title: Int. J. Pattern Recognit. Artif. Intell. – volume: 43 start-page: 321 issue: 4 year: 2017 end-page: 339 ident: C30 article-title: An improved SDA based defect prediction framework for both within-project and cross-project class-imbalance problems publication-title: IEEE Trans. Softw. Eng. – year: 2018 ident: C20 article-title: Deep semantic feature learning for software defect prediction publication-title: IEEE Trans. Softw. Eng. – volume: 19 start-page: 2045 issue: 9 year: 2017 end-page: 2055 ident: C17 article-title: Video captioning with attention-based LSTM and semantic consistency publication-title: IEEE Trans. Multimed. – volume: 17 start-page: 275 issue: 3 year: 2014 end-page: 306 ident: C32 article-title: Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy publication-title: Evol. Comput. – volume: SE-2 start-page: 308 issue: 4 year: 1976 end-page: 320 ident: C6 article-title: A complexity measure publication-title: IEEE Trans. Softw. Eng. – volume: 62 start-page: 67 year: 2015 end-page: 77 ident: C29 article-title: Negative samples reduction in cross-company software defects prediction publication-title: Inf. Softw. Technol. – volume: 5 start-page: 157 issue: 2 year: 1994 end-page: 166 ident: C21 article-title: Learning long-term dependencies with gradient descent is difficult publication-title: IEEE Trans. Neural Netw. – volume: 9 start-page: 1735 issue: 8 year: 1997 end-page: 1780 ident: C22 article-title: Long short-term memory publication-title: Neural Comput. – year: 2011 – volume: 9 start-page: 1 issue: 1 year: 2018 end-page: 19 article-title: Combining data preprocessing methods with imputation techniques for software defect prediction publication-title: Int. J. Open Source Softw. Processes (IJOSSP) – volume: 12 start-page: 161 issue: 3 year: 2018 end-page: 175 article-title: Progress on approaches to software defect prediction publication-title: IET Softw. – start-page: 308 issue: 4 year: 1976 end-page: 320 article-title: A complexity measure publication-title: IEEE Trans. Softw. Eng. – volume: 19 start-page: 2045 issue: 9 year: 2017 end-page: 2055 article-title: Video captioning with attention-based LSTM and semantic consistency publication-title: IEEE Trans. Multimed. – volume: 20 start-page: 476 issue: 6 year: 1994 end-page: 493 article-title: A metrics suite for object oriented design publication-title: IEEE Trans. Softw. Eng. – start-page: 71 year: 2019 end-page: 74 article-title: Research on patent text classification based on word2vec and lstm – volume: 19 start-page: 1409 issue: 6 year: 2015 end-page: 1432 article-title: Improving random forest and rotation forest for highly imbalanced datasets publication-title: Intell. Data Anal. – year: 2010 article-title: Recurrent neural network based language model – volume: 9 start-page: 1735 issue: 8 year: 1997 end-page: 1780 article-title: Long short-term memory publication-title: Neural Comput. – year: 2016 – volume: 62 start-page: 67 year: 2015 end-page: 77 article-title: Negative samples reduction in cross-company software defects prediction publication-title: Inf. Softw. Technol. – year: 1977 – year: 2018 – year: 2014 – start-page: 45 year: 2013 end-page: 54 article-title: Learning from open-source projects: an empirical study on defect prediction – start-page: 9 year: 2010 article-title: Towards identifying software project clusters with regard to defect prediction – start-page: 4574 year: 2016 end-page: 4582 article-title: Latent attention for ifthen program synthesis – start-page: 297 year: 2016 end-page: 308 article-title: Automatically learning semantic features for defect prediction – start-page: 99 year: 2015 end-page: 108 article-title: Online defect prediction for imbalanced data – start-page: 837 year: 2012 end-page: 847 article-title: On the naturalness of software – volume: 33 issue: 12 year: 2019 article-title: An investigation of imbalanced ensemble learning methods for cross-project defect prediction publication-title: Int. J. Pattern Recognit. Artif. Intell. – volume: 5 start-page: 157 issue: 2 year: 1994 end-page: 166 article-title: Learning long-term dependencies with gradient descent is difficult publication-title: IEEE Trans. Neural Netw. – volume: 17 start-page: 275 issue: 3 year: 2014 end-page: 306 article-title: Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy publication-title: Evol. Comput. – volume: 43 start-page: 321 issue: 4 year: 2017 end-page: 339 article-title: An improved SDA based defect prediction framework for both within-project and cross-project class-imbalance problems publication-title: IEEE Trans. Softw. Eng. – start-page: 37 year: 2017 end-page: 42 article-title: Convolutional neural networks on assembly code for predicting software defects – year: 2017 article-title: Software defect prediction via convolutional neural network – start-page: 273 year: 2013 end-page: 278 article-title: Hybrid speech recognition with deep bidirectional LSTM – start-page: 547 year: 2015 end-page: 553 article-title: Building program vector representations for deep learning – start-page: 457 year: 2015 end-page: 463 article-title: The effect of data sampling when using random forest on imbalanced bioinformatics data – year: 2018 article-title: Deep semantic feature learning for software defect prediction publication-title: IEEE Trans. Softw. Eng. – year: 2019 – start-page: 318 year: 2017 end-page: 328 article-title: Software defect prediction via convolutional neural network – year: 2015 – year: 2012 article-title: LSTM neural networks for language modeling – year: 2013 – ident: e_1_2_9_7_1 doi: 10.1109/TSE.1976.233837 – ident: e_1_2_9_17_1 doi: 10.1109/ASRU.2013.6707742 – ident: e_1_2_9_23_1 doi: 10.1162/neco.1997.9.8.1735 – ident: e_1_2_9_3_1 doi: 10.1109/IESYS.2017.8233558 – ident: e_1_2_9_29_1 doi: 10.1007/978-3-319-25159-2_49 – ident: e_1_2_9_19_1 doi: 10.1049/iet-sen.2017.0148 – ident: e_1_2_9_6_1 – ident: e_1_2_9_25_1 – ident: e_1_2_9_34_1 doi: 10.1109/IRI.2015.76 – year: 2018 ident: e_1_2_9_21_1 article-title: Deep semantic feature learning for software defect prediction publication-title: IEEE Trans. Softw. Eng. – ident: e_1_2_9_4_1 doi: 10.1049/cp.2013.2313 – ident: e_1_2_9_8_1 doi: 10.1109/32.295895 – ident: e_1_2_9_22_1 doi: 10.1109/72.279181 – ident: e_1_2_9_31_1 doi: 10.1109/TSE.2016.2597849 – ident: e_1_2_9_5_1 doi: 10.1142/S0218001419590377 – ident: e_1_2_9_12_1 doi: 10.1145/1868328.1868342 – ident: e_1_2_9_14_1 doi: 10.21437/Interspeech.2010-343 – ident: e_1_2_9_11_1 doi: 10.1109/ESEM.2013.20 – ident: e_1_2_9_28_1 doi: 10.1109/ISCID.2018.00023 – ident: e_1_2_9_24_1 – ident: e_1_2_9_38_1 – ident: e_1_2_9_10_1 – ident: e_1_2_9_27_1 – ident: e_1_2_9_9_1 doi: 10.1145/2884781.2884804 – ident: e_1_2_9_20_1 doi: 10.1109/QRS.2017.42 – ident: e_1_2_9_35_1 doi: 10.3233/IDA-150789 – ident: e_1_2_9_15_1 doi: 10.21437/Interspeech.2012-65 – ident: e_1_2_9_18_1 doi: 10.1109/TMM.2017.2729019 – ident: e_1_2_9_13_1 doi: 10.1109/ICSE.2012.6227135 – ident: e_1_2_9_26_1 – ident: e_1_2_9_33_1 doi: 10.1162/evco.2009.17.3.275 – ident: e_1_2_9_37_1 – ident: e_1_2_9_30_1 doi: 10.1016/j.infsof.2015.01.014 – volume-title: Data mining: concepts and techniques year: 2011 ident: e_1_2_9_36_1 – ident: e_1_2_9_2_1 doi: 10.4018/IJOSSP.2018010101 – ident: e_1_2_9_16_1 doi: 10.3115/v1/D14-1179 – ident: e_1_2_9_32_1 doi: 10.1109/ICSE.2015.139 |
| SSID | ssj0055539 |
| Score | 2.479199 |
| Snippet | Software quality plays an important role in the software lifecycle. Traditional software defect prediction approaches mainly focused on using hand-crafted... Software quality plays an important role in the software lifecycle. Traditional software defect prediction approaches mainly focused on using hand‐crafted... |
| SourceID | crossref wiley iet |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 443 |
| SubjectTerms | AST node sequence contextual features defective code feature extraction human languages learning (artificial intelligence) long short-term memory network LSTM machine learning techniques numerical vectors open source projects program abstract syntax trees program debugging program diagnostics programming languages public domain software recurrent neural nets Research Article semantic features software defect prediction approaches software lifecycle software quality structural information trees (mathematics) word embedding techniques |
| Title | Software defect prediction via LSTM |
| URI | http://digital-library.theiet.org/content/journals/10.1049/iet-sen.2019.0149 https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fiet-sen.2019.0149 |
| Volume | 14 |
| WOSCitedRecordID | wos000588424400012&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 1751-8814 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055539 issn: 1751-8806 databaseCode: WIN dateStart: 20130101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 1751-8814 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0055539 issn: 1751-8806 databaseCode: 24P dateStart: 20130101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dS8MwEA9z-uCL8xPnFwXFByG6Js2aPOqwKOgYbLK9lSS9wkDm6Ob89730YzCECeJLKc0H5S6_-117yR0hV8hYyBotoNyd3wqskVTLRFFkEwNtzdpgi2ITYbcrRyPVq5FOdRamyA-x_OHmkJHbawdwbYoqJOjUohLHMKczcClMfXXrHP0Nsun7PHRLmwW9yhwLIfJyYkiTPpXSD5ahTXX3Y4oVctrA5lWXNeecqPEvb7tLdkqX07sv1sgeqcFknzSqcg5eie4DctlHk_ylM_AScJs8vGnmojhOc95irL2X_uD1kLxFj4POEy1rKFDLw1BQ0Q4ZT5hO0FXiqTDScmk1gEhCyzmkiFdEtEm1wdZAabSQ1mhtGYqKMx_4EalPPiZwTDzpgzLQaqVMJehEKSPwNnVxS4PaTtMmaVXCi22ZYNzVuXiP80B3oGIUQoxCiJ0QYieEJrlZDpkW2TXWdb52z0qMzdZ15Lkqfp8y7kdD9hC50KQ4-dOoU7LN3Bd4viXwjNTn2Secky27mI9n2UW-EvE6fO5-A3g13ww |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dS8MwED-2KeiL8xPnZ0HxQaiuSbM2j36NidsYbOLeSpKmMJA5tjn_fe_abjCECeJbyRflLne_S-5yB3CJiIWoUbUup_dbvtGhq8JYuogm2tYUq1mTFZsI2u2w35edAjzO38Jk-SEWF24kGam-JgGnC-nswOlTksyBnboTSzlMPXlDln4R1nxEJwrsY35nro-FEGk9McRJzw1Dz1_4NuXtjyWW0KmI3cs2awo69fL__O42bOVGp3OX7ZIdKNjhLpTnBR2cXL734KKLSvlLja0TWwrzcEZj8uMQ75zZQDnNbq-1D6_1p95Dw82rKLiGB4FwRS1gPGYqRmOJJ0KHhodGWSviwHBuE5RYlGmdKI29vlSoI41WyjCkFWee5QdQGn4M7SE4oWelttVqwmSMZpTUAj8T8lxq5HeSVKA6p15k8hTjVOniPUpd3b6MkAgREiEiIkREhApcL6aMsvwaqwZfUVsuZZNVA3nKi9-XjLr1N3ZfJ-ekOPrTrHPYaPRazaj53H45hk1G5_E0QPAEStPxpz2FdTObDibjs3RbfgODKuHh |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dS8MwED_mFPHF-Ynzs6D4IFTXpFmbR3UWxTkGm7i3kqRXGMgc25z_vpe2GwxhgvhWmg_K7_K7u_SSO4ALslhkNWrocnt_yzc6dFWYSJesica6YnU0ebGJoNUKez3ZLkFjdhcmzw8x_-FmmZHpa0twHCZpvuH0bZLMPk7cMdocpp68tp7-Cqz6Isjoyfz2TB8LIbJ6YmQnPTcMPX8e25Q3P6ZYsE4r1Lzos2ZGJ6r8z-duwWbhdDq3-SrZhhIOdqAyK-jgFPzehfMOKeUvNUInQXvMwxmObBzHys6Z9pXT7HRf9uA1eujeP7pFFQXX8CAQrqgHjCdMJeQs8VTo0PDQKESRBIZzTImxxGmdKk2tvlSkI41WyjDCijMP-T6UBx8DPAAn9FBqrNVSJhNyo6QW9JjayKUmeadpFWoz9GJTpBi3lS7e4yzU7cuYQIgJhNiCEFsQqnA1HzLM82ss63xp3xUsGy_ryDNZ_D5l3Ine2F1kg5Pi8E-jzmC93Yji5lPr-Qg2mN2OZ-cDj6E8GX3iCayZ6aQ_Hp1mq_IbOJnhXA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Software+defect+prediction+via+LSTM&rft.jtitle=IET+software&rft.au=Deng%2C+Jiehan&rft.au=Lu%2C+Lu&rft.au=Qiu%2C+Shaojian&rft.date=2020-08-01&rft.pub=The+Institution+of+Engineering+and+Technology&rft.issn=1751-8814&rft.eissn=1751-8814&rft.volume=14&rft.issue=4&rft.spage=443&rft.epage=450&rft_id=info:doi/10.1049%2Fiet-sen.2019.0149&rft.externalDBID=10.1049%252Fiet-sen.2019.0149&rft.externalDocID=SFW2BF00295 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1751-8806&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1751-8806&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1751-8806&client=summon |