Software Development Effort Prediction Based on Collaborative Filtering
Software Development Effort Prediction Based on Collaborative Filtering
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基于协同过滤的软件开发工作量预测
DOI：
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发表时间：
2005
期刊：
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通讯作者：
Shin
中科院分区：
文献类型：
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作者：
Masateru Tsunoda;Naoki Ohsugi;Akito Monden;Ken;Shin
††† 为了预测软件开发工作量，本文提出了一种基于协同过滤（CF）的工作量预测方法，该方法使用过去软件开发项目中记录的各种软件指标作为输入。 CF 的一个优点是它可以使用包含大量缺失值的“有缺陷”的输入数据进行预测。然而，还没有研究提出将CF应用于软件工作量预测的方法。我们的建议包括三个步骤。第一步，我们对指标值进行标准化以使其值范围相等。在下一步中，我们使用归一化值计算目标（当前）项目和过去（已完成）项目之间的相似度。在最后一步中，我们通过使用每个项目的相似度作为权重计算高相似度项目（与目标项目相似）的工作量的加权和来估计目标项目的工作量。在评估我们方法的案例研究中，我们使用 1,081 个软件项目预测了测试过程的工作量，其中包括 14 个缺失值率为 60% 的指标，这些指标已在 NTT DATA Corporation 记录。结果，我们的方法的准确性表现出比传统方法（逐步多元回归模型）更好的性能；每个项目的平均准确率从 22.11 提高到 0.79。
††† To predict software development effort, this paper proposes an effort prediction method based on theCollaborative Filtering (CF ) which uses as input various software metrics recorded in past software development projects. The CF has an advantage that it can conduct a prediction using “defective” input data containing a large amount of missing values. There are, however, no researches which propose a method for applying the CF to Software effort prediction. Our proposal consists of three steps. In the first step, we normalize values of metrics to equalize their value range. In the next step, we compute the similarity between target (current) project and past (completed) project using normalized values. In the last step, we estimate the effort of target project by computing the weighted sum of efforts of high-similarity projects (that are similar to the target project) using the similarity of each project as a weight. In a case study to evaluate our method, we predicted the test process effort using 1,081 software projects including 14 metrics whose missing value rate is 60%, which have been recorded at NTT DATA Corporation. As a result, the accuracy of our method showed better performance than conventional methods (stepwise multiple regression models); and, the average accuracy per project was improved from 22.11 to 0.79.