Software-Effort Estimation: An Exploratory Study of Expert Performance

• 成果类型: Article
• 署名作者: Vicinanza, Steven S.; Mukhopadhyay, Tridas; Prietula, Michael J.
• 署名单位: Carnegie Mellon University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2.4.243
• 发表日期: 1991
• 页码: 243-262

摘要

An exploratory study was conducted (a) to examine whether experienced software managers could generate accurate estimates of effort required for proposed software projects and (b) to document the strategies they bring to bear in their estimations. Five experienced software project managers served as expert subjects for the study. Each manager was first asked to sort a set of 37 commonly-used estimation parameters according to the importance of their effect on effort estimation. Once this task was completed, the manager was then presented with data from ten actual software projects, one at a time, and asked to estimate the effort (in worker-months) required to complete the projects. The project sizes ranged from 39,000 to 450,000 lines of code and varied from 23 to 1,107 worker-months to complete. All managers were tested individually. The results were compared to those of two popular analytical models Function Points and COCOMO. Results show that the managers made more accurate estimates than the uncalibrated analytical models. Additionally, a process-tracing analysis revealed that the managers used two dissimilar types of strategies to solve the estimation problems algorithmic and analogical. Four managers invoked algorithmic strategies, which relied on the selection of a base productivity rate as an anchor that was further adjusted to compensate for productivity factors impacting the project. The fifth manager invoked analogical strategies, which did not rely on a base productivity rate as an anchor, but centered around the analysis of the Function Point data to assist in retrieving information regarding a similar, previously -managed project. The manager using the latter, analogical reasoning approach produced the most accurate estimates.