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基於軟體發展行動之軟體瑕疵預測技術
專利名稱 ACTION-BASED IN-PROCESS SOFTWARE DEFECT PREDICTION SOFTWARE DEFECT PREDICTION TECHNIQUES BASED ON SOFTWARE DEVELOPMENT ACTIVITIES
申請日 (校編號) 2007/04/17  (096002US)
專利證書號 7,856,616  美國
專利權人 國立中央大學
發明人 朱治平、張慶寶


技術摘要:
An action-based in-process software defect prediction (ABDP) applies classifying records of a performed action to predict whether subsequent actions cause defects in a project. A performed action is previously defined herein as an operation performed based on tasks in Work Breakdown Structure (WBS) of the project. Rather than focusing on the reported defects, ABDP discovers the patterns of the performed action that may cause defects composing a historic data set, and uses analytical results to predict whether the subsequent actions are likely to generate defects composing another historic data set. Once actions with high probability of causing the defects are identified, stakeholders review these actions carefully and take appropriate corrective actions to create a fresh performed action. The fresh performed action is continually appended to the historic data sets of amending the defects to construct a new prediction model for further subsequent actions.

解決的問題或達成的功效:
The present invention relates to a software defect prediction technique, and more particularly to provide a software defect prediction technique that is based on software development activities.

應用領域:
software defect prediction technique

適用產品:
工商顧問服務

IPC:
G06F9/44

Claim 1:
1. An action-based in-process software defect prediction (ABDP) comprising steps of:
applying classifying records of performed actions to predict whether subsequent actions cause defects in a project, wherein a performed action is previously defined as an operation performed based on tasks in Work Breakdown Structure (WBS) of the project;
discovering patterns of the performed action that causes defects that compose a first historic data set;
using analytical results to predict whether the subsequent actions are likely to generate the defects that compose a second historic data set;
reviewing and correcting the performed action and the subsequent actions by stakeholders to create a fresh performed action once the performed action and the subsequent actions with high probability of causing the defects;
appending the fresh performed action with the first and second historic data sets for amending the defects to construct a prediction model for further subsequent actions; and
functioning the prediction model to mine possible defects before executing the subsequent actions.

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