Software Risk Analysis with the use of Classification Techniques: A Review
Risk analysis and management is a critical aspect of the software development process. Various risks are associated with every phase of the software development lifecycle. The early identification of risks in each phase of software development coupled with mitigating plans can help to reduce the cost of the product and increase software quality. This study aims to explore various tools and techniques used in the literature of analyzing and managing risks. Most risk analysis techniques have been applied in the requirement analysis phase, so there is a scarcity of tools supporting automated risk analysis. Accommodating various types of risk factors to predict the software risks reduces the accuracy of the classifier.
Y. Hu, X. Zhang, E. W. T. Ngai, R. Cai, M. Liu, “Software project risk analysis using Bayesian Networks with causality constraints”, Decision Support Systems, Vol. 56, pp. 439–449, 2013
M. Ray, D. P. Mohapatra, “Risk analysis: a guiding force in the improvement of testing”, IET Software, Vol. 7, No. 1, pp. 29–46, 2013
Y. Hu, J. Du, X. Zhang, X. Hao, E. W. T. Ngai, M. Fan, M. Liu, “An integrative framework for intelligent software project risk planning”, Decision Support Systems, Vol. 55, No. 4, pp. 927–937, 2013
Z. Kremljak, C. Kafol, “Types of risk in a system engineering environment and software tools for risk analysis”, Procedia Engineering, Vol. 69, pp. 177–183, 2014
J. Li, M. Li, D. Wu, H. Song, “An integrated risk measurement and optimization model for trustworthy software process management”, Information Sciences, Vol. 191, pp. 47–60, 2012
S. Alhawari, L. Karadsheh, A. N. Talet, E. Mansour, “Knowledge-based risk management framework for information technology project”, International Journal of Information Management, Vol. 32, No. 1, pp. 50–65, 2012
S. Islam, H. Mouratidis, E. R. Weippl, “An empirical study on the implementation and evaluation of a goal-driven software development risk management model”, Information and Software Technology, Vol. 56, No. 2, pp. 117–133, 2014
C. Jin, S. W. Jin, “Applications of fuzzy integrals for predicting software fault-prone”, Journal of Intelligent Fuzzy Systems, Vol. 26, No. 2, pp. 721–729, 2014
W. M. Han, “Discriminating risky software project using neural networks”, Computer Standard & Interfaces, Vol. 40, pp. 15–22, 2015
O. F. Arar, K. Ayan, “Software defect prediction using cost-sensitive neural network”, Applied Soft Computing, Vol. 33, pp. 263–277, 2015
S. Chatterjee, B. Maji, “A new fuzzy rule based algorithm for estimating software faults in early phase of development”, Soft Computing, Vol. 20, No. 10, pp. 4023–4035, 2016
H. C. Liu, J. X. You, M. M. Shan, L. N. Shao, “Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach”, Soft Computing, Vol. 19, No. 4, pp. 1085–1098, 2015
R. Malhotra, “Comparative analysis of statistical and machine learning methods for predicting faulty modules”, Applied Soft Computing, Vol. 21, pp. 286–297, 2014
G. Czibula, Z. Marian, I. G. Czibula, “Software defect prediction using relational association rule mining”, Information Sciences, Vol. 264, pp. 260–278, 2014
W. Li, Z. Huang, Q. Li, “Three-way decisions based software defect prediction”, Knowlegde-Based Systems, Vol. 91, pp. 263–274, 2016
S. N. Bhukya, S. Pabboju, “Software engineering: Risk features in requirement engineering”, Cluster Computing, Vol. 22, No. S6, pp. 14789–14801, 2019
N. D. Linh, P. D. Hung, V. T. Diep, T. D. Tung, “Risk management in projects based on Open-Source Software”, 8th International Conference on Software and Computer Applications, Penang, Malaysia, February, 2019
K. Suresh, R. Dillibabu, “Designing a machine learning based software risk assessment model using Naïve Bayes algorithm”, TAGA Journal, Vol. 14, pp. 3141--3147, 2018.
A. E. Yamami, S. Ahriz, K. Mansouri, M. Qbadou, E. Illoussamen, “Representing IT projects risk management best practices as a metamodel”, Engineering, Technology & Applied Science Research, Vol. 7, No. 5, pp. 2062-2067, 2017
A. Chenarani, E. A. Druzhinin, “A quantitative measure for evaluating project uncertainty under variation and risk effects”, Engineering, Technology & Applied Science Research, Vol. 7, No. 5, pp. 2083-2088, 2017
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