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论文类型：期刊论文

发表时间：2016-08-01

发表刊物：JOURNAL OF SYSTEMS AND SOFTWARE

收录刊物：SCIE、EI、Scopus

卷号：118

页面范围：85-100

ISSN号：0164-1212

关键字：Software project management; Software process model; Model recommendation; Impact analysis; Machine learning

摘要：Among many factors that influence the success of a software project, the software process model employed is an essential one. An improper process model will be time consuming, error-prone and cost expensive, and further lower the quality of software. Therefore, how to choose an appropriate software process model is a very important problem for software development. Current works focus on the selection criteria and often lead to subjective results. In this paper, we propose a software process model recommendation method, to help project managers choose the most appropriate software process model for a new project at an early stage of development process according to historical software engineering data. The proposed method casts the process model recommendation into a classification problem. It first evaluates the different combinations of the alternative classification and attribute selection algorithms, and the best one is used to build the recommendation model with historical software engineering data; then, the constructed recommendation model is used to predict process models for a new software project with only a few data. We also analyze the mutual impacts between process models and different types of project factors, to further help managers locate the most suitable process model. We found process models are also responsible for defect count, defect severity and software change. Experiments on the data sets from 37 different development teams of different countries show that the average recommendation accuracy of our method reaches up to 82.5%, which makes it potentially useful in practice. (C) 2016 Elsevier Inc. All rights reserved.