AutoODC: Automated generation of orthogonal defect classifications
Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and...
Ausführliche Beschreibung
Autor*in: |
Huang, LiGuo [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2014 |
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Schlagwörter: |
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Anmerkung: |
© Springer Science+Business Media New York 2014 |
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Übergeordnetes Werk: |
Enthalten in: Automated software engineering - Springer US, 1994, 22(2014), 1 vom: 03. Juni, Seite 3-46 |
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Übergeordnetes Werk: |
volume:22 ; year:2014 ; number:1 ; day:03 ; month:06 ; pages:3-46 |
Links: |
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DOI / URN: |
10.1007/s10515-014-0155-1 |
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Katalog-ID: |
OLC205012077X |
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520 | |a Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list. | ||
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10.1007/s10515-014-0155-1 doi (DE-627)OLC205012077X (DE-He213)s10515-014-0155-1-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Huang, LiGuo verfasserin aut AutoODC: Automated generation of orthogonal defect classifications 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list. Orthogonal defect classification (ODC) Machine learning Natural language processing Text classification Ng, Vincent aut Persing, Isaac aut Chen, Mingrui aut Li, Zeheng aut Geng, Ruili aut Tian, Jeff aut Enthalten in Automated software engineering Springer US, 1994 22(2014), 1 vom: 03. Juni, Seite 3-46 (DE-627)182401634 (DE-600)1204691-7 (DE-576)043086187 0928-8910 nnns volume:22 year:2014 number:1 day:03 month:06 pages:3-46 https://doi.org/10.1007/s10515-014-0155-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.00 VZ AR 22 2014 1 03 06 3-46 |
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10.1007/s10515-014-0155-1 doi (DE-627)OLC205012077X (DE-He213)s10515-014-0155-1-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Huang, LiGuo verfasserin aut AutoODC: Automated generation of orthogonal defect classifications 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list. Orthogonal defect classification (ODC) Machine learning Natural language processing Text classification Ng, Vincent aut Persing, Isaac aut Chen, Mingrui aut Li, Zeheng aut Geng, Ruili aut Tian, Jeff aut Enthalten in Automated software engineering Springer US, 1994 22(2014), 1 vom: 03. Juni, Seite 3-46 (DE-627)182401634 (DE-600)1204691-7 (DE-576)043086187 0928-8910 nnns volume:22 year:2014 number:1 day:03 month:06 pages:3-46 https://doi.org/10.1007/s10515-014-0155-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.00 VZ AR 22 2014 1 03 06 3-46 |
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10.1007/s10515-014-0155-1 doi (DE-627)OLC205012077X (DE-He213)s10515-014-0155-1-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Huang, LiGuo verfasserin aut AutoODC: Automated generation of orthogonal defect classifications 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2014 Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list. Orthogonal defect classification (ODC) Machine learning Natural language processing Text classification Ng, Vincent aut Persing, Isaac aut Chen, Mingrui aut Li, Zeheng aut Geng, Ruili aut Tian, Jeff aut Enthalten in Automated software engineering Springer US, 1994 22(2014), 1 vom: 03. Juni, Seite 3-46 (DE-627)182401634 (DE-600)1204691-7 (DE-576)043086187 0928-8910 nnns volume:22 year:2014 number:1 day:03 month:06 pages:3-46 https://doi.org/10.1007/s10515-014-0155-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.00 VZ AR 22 2014 1 03 06 3-46 |
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AutoODC: Automated generation of orthogonal defect classifications |
abstract |
Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list. © Springer Science+Business Media New York 2014 |
abstractGer |
Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list. © Springer Science+Business Media New York 2014 |
abstract_unstemmed |
Abstract Orthogonal defect classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human-intensive and requires experts’ knowledge of both ODC and system domains. This paper presents AutoODC, an approach for automating ODC classification by casting it as a supervised text classification problem. Rather than merely applying the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts’ ODC experience and domain knowledge into the learning process via proposing a novel relevance annotation framework. We have trained AutoODC using two state-of-the-art machine learning algorithms for text classification, Naive Bayes (NB) and support vector machine (SVM), and evaluated it on both an industrial defect report from the social network domain and a larger defect list extracted from a publicly accessible defect tracker of the open source system FileZilla. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves overall accuracies of 82.9 % (NB) and 80.7 % (SVM) on the industrial defect report, and accuracies of 77.5 % (NB) and 75.2 % (SVM) on the larger, more diversified open source defect list. © Springer Science+Business Media New York 2014 |
collection_details |
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container_issue |
1 |
title_short |
AutoODC: Automated generation of orthogonal defect classifications |
url |
https://doi.org/10.1007/s10515-014-0155-1 |
remote_bool |
false |
author2 |
Ng, Vincent Persing, Isaac Chen, Mingrui Li, Zeheng Geng, Ruili Tian, Jeff |
author2Str |
Ng, Vincent Persing, Isaac Chen, Mingrui Li, Zeheng Geng, Ruili Tian, Jeff |
ppnlink |
182401634 |
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isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10515-014-0155-1 |
up_date |
2024-07-04T01:08:12.115Z |
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1803608697997361152 |
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