Kenichi Matsumoto

Incorporating Expert Judgment into Regression Models of Software Effort Estimation

One of the common problems in building an effort estimation model is that not all the effort factors are suitable as predictor variables. As a supplement of missing information in estimation models, this paper explores the project managers knowledge about the target project. We assume that the experts can judge the target projects productivity level based on his/her own expert knowledge about the project. We also assume that this judgment can be further improved, because using the experts judgment solely could incur subjective perception. This paper proposes a regression model building/selection method to address this challenge. In the proposed method, a fit dataset for model building is divided into two or three subsets by project productivity, and an estimation model is built on each data subset. The expert judges the productivity level of the target project and selects one of the models to be used. In the experiment, we used three datasets to evaluate the produced effort estimation models. In the experiment, we adjusted the error rate of the judgment and analyzed the relationship between the error rate and the estimation accuracy. As a result, the judgment-incorporating models produced significantly higher estimation accuracy than the conventional linear regression model, where the experts error rate is less than 37%.

Analysis of Donations in the Eclipse Project

Although development activities, such as submitting patches and working with bug reports, are common contributions in open source software (OSS) projects, making donations is also an important contribution. Some OSS development projects actively collect donations by preparing benefits for donors who promote donations. In this research, we study the Eclipse project to analyze donations. We analyzed donor lists and release dates, and found the following: (1) benefits can be motivations for donations, (2) although the number of developers is small in all donors, they donated more than others, and (3) new releases are triggers of donations, but bugs negatively affect the amount of donations.