Predicting defect priority based on neural networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations


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 languageEnglish (US)
Title of host publicationAdvanced Data Mining and Applications - 6th International Conference, ADMA 2010, Proceedings
Number of pages12
EditionPART 2
StatePublished - 2010
Event6th International Conference on Advanced Data Mining and Applications, ADMA 2010 - Chongqing, China
Duration: Nov 19 2010Nov 21 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6441 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th International Conference on Advanced Data Mining and Applications, ADMA 2010


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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