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
Gespeichert in:
| 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 |
|---|---|
| Übergeordnetes Werk: |
volume:22 ; year:2014 ; number:1 ; day:03 ; month:06 ; pages:3-46 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10515-014-0155-1 |
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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. | ||
| 650 | 4 | |a Orthogonal defect classification (ODC) | |
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| 700 | 1 | |a Tian, Jeff |4 aut | |
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autoodc: automated generation of orthogonal defect classifications |
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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 |
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AutoODC: Automated generation of orthogonal defect classifications |
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Ng, Vincent Persing, Isaac Chen, Mingrui Li, Zeheng Geng, Ruili Tian, Jeff |
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