A Probabilistic Software Risk Assessment and Estimation Model for Software Projects

Abstract Software risk management plays a vital role in successful software project management. In fact, all the phases of the software development life cycle (SDLC) are potential sources of software risks since it involves hardware, software, technology, people, cost, and schedule. There are a number of software risk factors that affect the whole software development process. However, finding the correlation between risk factors and project outcome is the main focus of present research on software risk analysis. In this paper, a probabilistic software risk estimation model is proposed using Bayesian Belief Network (BBN) that focuses on the top software risk indicators for risk assessment in software development projects. In order to assess the constructed model, an empirical experiment has been performed, based on the data collected from software development projects used by an organization.

[1]  Mark Keil,et al.  Software project risks and their effect on outcomes , 2004, CACM.

[2]  B. Boehm.  Software risk management: principles and practices , 1991, IEEE Software.

[3]  Yong Hu,et al.  Software project risk analysis using Bayesian networks with causality constraints , 2013, Decis. Support Syst..

[4]  Dilip Kumar Yadav,et al.  A method for developing node probability table using qualitative value of software metrics , 2015, Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT).

[5]  June M. Verner,et al.  Case study: factors for early prediction of software development success , 2002, Inf. Softw. Technol..

[6]  Shareeful Islam,et al.  Software development risk management model: a goal driven approach , 2009, ESEC/FSE Doctoral Symposium '09.

[7]  Hany H. Ammar,et al.  Model-based performance risk analysis , 2005, IEEE Transactions on Software Engineering.

[8]  Yongtae Park,et al.  Large engineering project risk management using a Bayesian belief network , 2009, Expert Syst. Appl..

[9]  Chin-Feng Fan,et al.  BBN-based software project risk management , 2004, J. Syst. Softw..

[10]  Suresh L. Konda,et al.  Taxonomy-Based Risk Identification , 1993 .

[11]  Steve McConnell,et al.  Software Project Survival Guide , 1997 .

[12]  F. Michael Dedolph,et al.  The neglected management activity: Software risk management , 2003, Bell Labs Technical Journal.

[13]  Francisco Javier Díez,et al.  Parameter adjustment in Bayes networks. The generalized noisy OR-gate , 1993, UAI.

[14]  Gary Klein,et al.  Software development risks to project effectiveness , 2000, J. Syst. Softw..

[15]  Mohd Sadiq,et al.  A systematic approach for the estimation of software risk and cost using esrcTool , 2013, CSI Transactions on ICT.

[16]  Norman E. Fenton,et al.  Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

[17]  Hermano Perrelli de Moura,et al.  Defining Indicators for Risk Assessment in Software Development Projects , 2013, CLEI Electron. J..

[18]  Peter Kaiser,et al.  An industrial case study of implementing software risk management , 2001, ESEC/FSE-9.

[19]  Max Henrion,et al.  Efficient Search-Based Inference for noisy-OR Belief Networks: TopEpsilon , 1996, UAI.

[20]  Barry W. Boehm,et al.  Bayesian Analysis of Empirical Software Engineering Cost Models , 1999, IEEE Trans. Software Eng..

[21]  Lars Mathiassen,et al.  Attention-shaping tools, expertise, and perceived control in IT project risk assessment , 2007, Decis. Support Syst..