Predicting defect priority based on neural networks

Lian Yu; Wei Tek Tsai; Wei Zhao; Fang Wu

doi:10.1007/978-3-642-17313-4_35

Predicting defect priority based on neural networks

Lian Yu^*
, Wei Tek Tsai
, Wei Zhao
, Fang Wu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

52 Scopus citations

Abstract

Existing defect management tools provide little information on how important/urgent for developers to fix defects reported. Manually prioritizing defects is time-consuming and inconsistent among different people. To improve the efficiency of troubleshooting, the paper proposes to employ neural network techniques to predict the priorities of defects, adopt evolutionary training process to solve error problems associated with new features, and reuse data sets from similar software systems to speed up the convergence of training. A framework is built up for the model evaluation, and a series of experiments on five different software products of an international healthcare company to demonstrate the feasibility and effectiveness.

Original language	English
Title of host publication	Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings
Pages	356-367
Number of pages	12
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-17313-4_35
State	Published - 2010
Externally published	Yes
Event	6th International Conference on Advanced Data Mining and Applications, ADMA 2010 - Chongqing, China Duration: 19 Nov 2010 → 21 Nov 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6441 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Advanced Data Mining and Applications, ADMA 2010
Country/Territory	China
City	Chongqing
Period	19/11/10 → 21/11/10

Keywords

Defect priority
artificial neural network
attribute dependency
convergence of training
evolutionary training

Access to Document

10.1007/978-3-642-17313-4_35

Cite this

Yu, L., Tsai, W. T., Zhao, W., & Wu, F. (2010). Predicting defect priority based on neural networks. In Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings (PART 2 ed., pp. 356-367). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6441 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-17313-4_35

@inproceedings{dae39d3ba2314192b8113c3549f2365f,

title = "Predicting defect priority based on neural networks",

abstract = "Existing defect management tools provide little information on how important/urgent for developers to fix defects reported. Manually prioritizing defects is time-consuming and inconsistent among different people. To improve the efficiency of troubleshooting, the paper proposes to employ neural network techniques to predict the priorities of defects, adopt evolutionary training process to solve error problems associated with new features, and reuse data sets from similar software systems to speed up the convergence of training. A framework is built up for the model evaluation, and a series of experiments on five different software products of an international healthcare company to demonstrate the feasibility and effectiveness.",

keywords = "Defect priority, artificial neural network, attribute dependency, convergence of training, evolutionary training",

author = "Lian Yu and Tsai, \{Wei Tek\} and Wei Zhao and Fang Wu",

year = "2010",

doi = "10.1007/978-3-642-17313-4\_35",

language = "英语",

isbn = "3642173128",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "356--367",

booktitle = "Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings",

edition = "PART 2",

note = "6th International Conference on Advanced Data Mining and Applications, ADMA 2010 ; Conference date: 19-11-2010 Through 21-11-2010",

}

Yu, L, Tsai, WT, Zhao, W & Wu, F 2010, Predicting defect priority based on neural networks. in Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6441 LNAI, pp. 356-367, 6th International Conference on Advanced Data Mining and Applications, ADMA 2010, Chongqing, China, 19/11/10. https://doi.org/10.1007/978-3-642-17313-4_35

Predicting defect priority based on neural networks. / Yu, Lian; Tsai, Wei Tek; Zhao, Wei et al.
Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings. PART 2. ed. 2010. p. 356-367 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6441 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predicting defect priority based on neural networks

AU - Yu, Lian

AU - Tsai, Wei Tek

AU - Zhao, Wei

AU - Wu, Fang

PY - 2010

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N2 - Existing defect management tools provide little information on how important/urgent for developers to fix defects reported. Manually prioritizing defects is time-consuming and inconsistent among different people. To improve the efficiency of troubleshooting, the paper proposes to employ neural network techniques to predict the priorities of defects, adopt evolutionary training process to solve error problems associated with new features, and reuse data sets from similar software systems to speed up the convergence of training. A framework is built up for the model evaluation, and a series of experiments on five different software products of an international healthcare company to demonstrate the feasibility and effectiveness.

AB - Existing defect management tools provide little information on how important/urgent for developers to fix defects reported. Manually prioritizing defects is time-consuming and inconsistent among different people. To improve the efficiency of troubleshooting, the paper proposes to employ neural network techniques to predict the priorities of defects, adopt evolutionary training process to solve error problems associated with new features, and reuse data sets from similar software systems to speed up the convergence of training. A framework is built up for the model evaluation, and a series of experiments on five different software products of an international healthcare company to demonstrate the feasibility and effectiveness.

KW - Defect priority

KW - artificial neural network

KW - attribute dependency

KW - convergence of training

KW - evolutionary training

UR - https://www.scopus.com/pages/publications/78650209732

U2 - 10.1007/978-3-642-17313-4_35

DO - 10.1007/978-3-642-17313-4_35

M3 - 会议稿件

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SN - 9783642173127

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings

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ER -

Yu L, Tsai WT, Zhao W, Wu F. Predicting defect priority based on neural networks. In Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings. PART 2 ed. 2010. p. 356-367. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-17313-4_35

Predicting defect priority based on neural networks

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