Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

Yong Hu, Bin Feng, Xizhu Mo, Xiangzhou Zhang, Wai Ting Ngai, Ming Fan, Mei Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)

Abstract

Nowadays software is mainly developed through outsourcing and it has become one of the most important business practice strategies for the software industry. However, outsourcing projects are often affiliated with high failure rate. Therefore to ensure success in outsourcing projects, past research has aimed to develop intelligent risk prediction models to evaluate the success rate and cost-effectiveness of software projects. In this study, we first summarized related work over the past 20 years and observed that all existing prediction models assume equal misclassification costs, neglecting actual situations in the management of software projects. In fact, overlooking project failure is far more serious than the misclassification of a success-prone project as a failure. Moreover, ensemble learning, a technique well-recognized to improve prediction performance in other fields, has not yet been comprehensively studied in software project risk prediction. This study aims to close the research gaps by exploring cost-sensitive analysis and classifier ensemble methods. Comparative analysis with T-test on 60 different risk prediction models using 327 outsourced software project samples suggests that the ideal model is a homogeneous ensemble model of decision trees (DT) based on bagging. Interestingly, DT underperformed Support Vector Machine (SVM) in accuracy (i.e.; assuming equal misclassification cost), but outperformed in cost-sensitive analysis under the proposed framework. In conclusion, this study proposes the first cost-sensitive and ensemble-based hybrid modeling framework (COSENS) for software project risk prediction. In addition, it establishes a new rigorous evaluation standard for assessing software risk prediction models by considering misclassification costs.
Original languageEnglish
Pages (from-to)11-23
Number of pages13
JournalDecision Support Systems
Volume72
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • COSENS
  • Cost-sensitive
  • Ensemble
  • Outsourced software project
  • Risk management
  • Risk prediction

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

Cite this