Learning to identify software defects when historical sample modules are unavailable

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• Dr. Ming Li, LAMDA Group, National Key Laboratory for Novel Software Technology, Nanjing University
• Monday 01 September 2014, 16:00-17:00
• Haworth 203.

If you have a question about this talk, please contact Leandro Minku.

Host: Dr. Shan He

Software Systems are becoming larger and more complex, which places a big challenge on software quality assurance because it is almost infeasible to conduct extensive code inspection or testing for every software module. Thus, constructing a predictor to identify the software modules that are likely to be defect-prone can enable a guided testing over those suspicious modules, which essentially help to find and fix more software defects under limited resource for testing. Machine learning is an effective way to construct a predictors from a large amount of historical sample modules. However, historical sample modules might not be always available or useful for identifying for defect patterns for a newly developed software project. In this case, how to learn an effective predictor to identify a defect-prone modules? In this talk, we will discuss the attempts to address this problem with advances in machine learning.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

This talk is included in these lists:

• Artificial Intelligence and Natural Computation seminars
• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• Haworth 203
• computer sience

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