Proposed Risk Management Model to Handle Changing Requirements

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Author(s)

Mohammad D. Aljohani 1,* Rizwan Qureshi 1

1. Faculty of Computing and Information Technology, King Abdul-Aziz University, Jeddah, Saudi Arabia

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2019.05.03

Received: 19 May 2019 / Revised: 3 Jun. 2019 / Accepted: 20 Jun. 2019 / Published: 8 Sep. 2019

Index Terms

Requirements Change Management, Risk Management, Case Study, Cost Estimation Models.

Abstract

The change in requirements while construction of a software may bring into several risks like over budget and extra schedule. The changes in requirements are considered as a high risk to fail the software projects. A good project manager always incorporates risk management paradigm to manage the risks of changing requirements. This research uses available statistical techniques to estimate the cost of risk management with respect to the changing requirement. In addition, a hybrid cost estimation model is proposed using action strategy model to counteract, mitigate and manage the risks of changing requirements. The proposed model is validated using an industrial case study in Saudi Electricity Company (SEC) to conclude the results. The results are found supportive because the proposed model shows significant improvement to estimate the costs of changing requirements as compared to the existing cost estimation models.

Cite This Paper

Mohammad D. AlJohani, Rizwan Qureshi. " Proposed Risk Management Model to Handle Changing Requirements", International Journal of Education and Management Engineering(IJEME), Vol.9, No.5, pp.18-25, 2019. DOI: 10.5815/ijeme.2019.05.03

Reference

[1]Newton P. Managing Project Risk Project Skills. BookBoon.com; 2015.

[2]Balaji N, Shivakumar N, and Ananth VV. Software cost estimation using function point with non algorithmic approach. Glob J Comput Sci Tech 2013;13:1-7.

[3]Chowdhury AAM, and Arefeen S. Software risk management: importance and practices. Int J Computer and Information Technology (IJCIT) 2011;2:49-54. 

[4]Mittal S. Risk Analysis and Mitigation Steps in Different Phases of Software Development. Int J Sci Res 2013;2:241-243.

[5]Nolan AJ, Abrahao S, Clements PC, and Pickard A. (2011) Requirements Uncertainty in a Software Product Line. Proc 15th Int Conf Software Product Line, Munich, Germany, 223-231.

[6]Davey B and Parker KR. Requirements elicitation problems: a literature analysis. Issues Informing Sci Inf Tech 2015;12:71-82.

[7]Fu Y, Li M, and Chen F. Impact propagation and risk assessment of requirement changes for software development projects based on design structure matrix. Int J Proj Manag 2012;30:363-373.

[8]Devesh S and Priyank DH. Effective Risk Management Techniques in Development of IVR Software. Int J Comput Sci Tech 2013;4:87-90.

[9]Hijazi H, Alqrainy S, Muaidi H, and Khdour T. Risk factors in software development phases. Eur. Sci. J. ESJ 2014;10:213-231.

[10]Nerkar LR  and Yawalkar PM. Software Cost Estimation using Algorithmic Model and Non-Algorithmic Model a Review.  Int J Comput App 2014;2:4-7.

[11]Bhatti MW, Hayat F, Ehsan N, Ishaque A, Ahmed S, and Sarwar SZ (2010). An investigation of changing requirements with respect to development phases of a software project. Proc Int Conf Comput Info Sys and Ind Mgmt App (CISIM), Krackow, Poland, 323-327.

[12]Gupta A, Mishra N, and S. Kushwaha DS (2014). Rule based test case reduction technique using decision table. Proc Int Conf Adv Comput, Gurgaon, India, 1398–1405.

[13]Suri PK and Ranjan P. Comparative analysis of software effort estimation techniques. Int J Comput App 2012;48:12-19.

[14]Patil LV, Waghmode RM, Joshi SD, and Khanna V (2014). Generic model of software cost estimation: A hybrid approach. Proc Int Conf Adv Comput, Gurgaon, India, 1379–1384.

[15]Waghmode RM, Patil LV., and Joshi SD. A Collective Study of PCA and Neural Network based on COCOMO for Software Cost Estimation. Int J Comput App 2013;74:25-30.

[16]Madheswaran M and Sivakumar D (2014). Enhancement of prediction accuracy in COCOMO model for software project using neural network. 5th Int Conf Computing, Commun and Networking Techs (ICCCNT), Hefei, China,1-5.

[17]Saroha M and Sahu S (2015). Tools & methods for software effort estimation using use case points model-A review. Proc Int Conf Commun Auto Comput, Noida, India, 874-879.

[18]Kaushik A, Chauhan A, Mittal D, and Gupta S. COCOMO Estimates Using Neural Networks. Int J Intell Sys App 2012;4:22-28.

[19]Anandhi V and Chezian RM (2014). Regression Techniques in Software Effort Estimation Using COCOMO Dataset. Proc Int Conf Intell Comput App, Coimbatore, India, 353-357.

[20]Lilja KK, Laakso K, and Palomäki J (2011). Using the Delphi method. Proc Int Conf Technology Management in the Energy Smart World (PICMET), Portland, OR, USA, 1-10.

[21]Nielsen K, Software Estimation using a Combination of Techniques. USA: Proj Manag Inst; 2013.

[22]Lavazza L and Morasca S (2012). Software effort estimation with a generalized robust linear regression technique. Proc 16th Int Conf Eval & Assessment Software Engineering, Ciudad Real, Spain, 206-215.

[23]Marandi AK and Khan DA. An Impact of Linear Regression Models for Improving the Software Quality 

 with Estimated Cost. Procedia Comput Sci 2015;54:335-342.

[24]Karna H and Gotovac S (2015). Modeling expert effort estimation of software projects. Proc 22nd Int Conf Software, Telecommun and Comp Networks, Split, Croatia, 356-360.

[25]Pandey P (2013). Analysis of the Techniques for Software Cost Estimation. Proc 3rd Int Conf Adv Computing and Commun Techs, Rohtak, India,16-19.

[26]Bhuvaneswari PTV, Gayathri S, and Priyadharshini AS (2014). Activity Estimation Using Regression Technique.  Proc Int Conf Comput Intelli and Commun Networks, Bhopal, India, 1177–1183.

[27]Kaushik A., Soni AK, and Soni R (2012). An adaptive learning approach to software cost estimation. Proc Int Conf Comput and Commun Syss (NCCCS), Durgapur, India, 1-6.

[28]Kumar SA and Kumar TA. Study the impact of Requirements management Characteristics in global software development projects: An Ontology based approach. Int J Softw Eng Appl 2011;2:107-125.