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

Hu, Yong; Feng, Bo; Mo, Xizhu; Zhang, Xiangzhou; Ngai, Eric W.T.; Fan, Ming; Liu, Mei

doi:10.1016/j.dss.2015.02.003

Cited by 29 publications

(14 citation statements)

References 55 publications

(101 reference statements)

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“…Besides, AHP has ability to judge the consistency of pairwise comparison matrix to show its potential conflicts in decision-making process. AHP has been widely used in supply chain management [26,27,28], human reliability analysis [29], cloud computing [30], energy planning [31,32,33], site selection [34,35,36], software assessment [37,38] and performance evaluation [39,40]. AHP can be extended further by other theories and methods such as fuzzy number [41,42], choquet integral [43], k-means algorithm [44], VIKOR [45] and TOPSIS [46,47].…”

Section: Analytic Hierarchy Process (Ahp)mentioning

confidence: 99%

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

et al. 2018

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Pairwise comparison matrix as a crucial component of AHP, presents the preference relations among alternatives. However, in many cases, the pairwise comparison matrix is difficult to complete, which obstructs the subsequent operations of the classical AHP. In this paper, based on DEMATEL which has ability to derive the total relation matrix from direct relation matrix, a new completion method for incomplete pairwise comparison matrix is proposed. The proposed method provides a new perspective to estimate the missing values with explicit physical meaning. Besides, the proposed method has low computational cost. This promising method has a wide application in multi-criteria decision-making.

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Section: Analytic Hierarchy Process (Ahp)mentioning

confidence: 99%

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

et al. 2018

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“…The authors of [83][84][85] presented a test framework of the source code metrics and chose the best metric set for the model's performance. The cost estimation method is used to test the predictive failure models.…”

Section: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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“…Considering the specialty of the outsourced software project, we use three first-level attributes to make decision according to Refs. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]: project complexity risks, contractor risks, and customer support and collaboration risks. (11), we obtain the results as follows:…”

Section: Methodsmentioning

confidence: 99%

A Novel Weighted Average Index Method of Interval Valued Intuitionistic Fuzzy Sets and Its Application to Outsourced Software Project Risk Assessment

Zhen-hua

Chen

et al. 2016

Advances in Intelligent Systems and Computing

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A novel weighted average index model of interval valued intuitionistic fuzzy sets (IVIFS) is presented in this paper. By analyzing the interval of membership degree, the interval of non-membership degree and the interval of hesitancy degree, we provide two weighted arithmetic average indexes and two geometric average indexes of IVIFS. And we prove some mathematical properties of these average indexes. Finally, a multiple attribute decision making example applied to outsourced software project risk assessment is given to demonstrate the application of these statistical indexes. The simulation results show that the evaluation methods on these average indexes are all effective.

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

Cited by 29 publications

References 55 publications

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP

Software Project Management Using Machine Learning Technique—A Review

A Novel Weighted Average Index Method of Interval Valued Intuitionistic Fuzzy Sets and Its Application to Outsourced Software Project Risk Assessment

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