Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

Li, Xiaoqing; Jiang, Qingquan; Hsu, Maxwell K.; Chen, Qinglan

doi:10.3390/su11174513

Cited by 18 publications

(9 citation statements)

References 19 publications

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“…Previous principles and any best practice supporting applying those principles eliminate or mitigate the negative impact of risks (threats) or promote positive risks (opportunities) that often lead to project success, challenges, or failure [19].…”

Section: A Risk In Software Development Projectsmentioning

confidence: 99%

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Enhance Risks Management of Software Development Projects in Concurrent Multi-Projects Environment to Optimize Resources Allocation Decisions

Alharbi¹,

Alyoubi²,

Altuwairiqi³

et al. 2021

IJACSA

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In software development project management, Risk management represents critical knowledge and skills at the level of a single software project and at the enterprise level, which executes multiple software projects concurrently. The best decision of Risk management contributes to optimizing resource allocation at the enterprise level for achieving its goals. Therefore, the issue needs centralized risk management at the enterprise level as a whole and not for each project. Risk management is implemented through several stages and using different methods. Various studies deal with multiple aspects of software management. This research provides an analytical view of risk assessment in multi-environment software development projects that take place simultaneously. The study uses a public dataset previously used in previous research for several simultaneous projects in one organization. It describes the multisoftware project's risks through 12 variables. A comparative analysis uses classification methods ( Random Forest-TreesJ48 -REP Tree -Simple Logistic) to assess risks and put them in central view. The research experiment has proven high accuracy in determining risk levels in a multi-project environment, reaching approximately 98%, using the REP tree technique.

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Section: A Risk In Software Development Projectsmentioning

confidence: 99%

“…There fore , organizations must assess and analyze the risks of softwa re projects to be planned by more accurately identifying potent ial risks and adopting scientific risk mitigation methods . Softw are risk factors may be diverse and complex, and historical data may be uncertain and unregulated [19].…”

Section: Introductionmentioning

confidence: 99%

Enhance Risks Management of Software Development Projects in Concurrent Multi-Projects Environment to Optimize Resources Allocation Decisions

Alharbi¹,

Alyoubi²,

Altuwairiqi³

et al. 2021

IJACSA

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show abstract

“…In order to establish the number of neurons in the hidden layer, the cut-and-try method was employed, using the formula 𝑚 = √𝑛 + 𝑙 + 𝑎. Where m is the number of hidden neurons, the number of input and output neurons are denoted by n and l respectively and a, a constant, is established as a ∈ [1,10] and m< n-1; thus, given a neural network with ten input and one output neurons, m ∈ [5,9] and in the reduced neural network with five input neurons m ∈ [3,4]. As an activation function(used to calculate the layer output given its net input), the hyperbolic tangent sigmoid transfer function was employed due to its steeper derivatives in calculating the gradient compared to other activation functions considered for this study.…”

Section: Backpropagation Neural Network (Bpnn)mentioning

confidence: 99%

Prediction of Student Performance Using Rough Set Theory And Backpropagation Neural Networks

Madeira

Tasci

Çelebi

2021

ESJ

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With the rise of web-based education systems and the increased use of information systems in education institutions, the amount of data recorded on student performance and behavior has increased exponentially. Thus, bringing about a large number of contributions to the field of educational research, which in itself contributed to the further evolution off the field in the last two decades alone, with terms such as Educational Data Mining (EDM), Learning Analytics, Data-driven Education, Teaching Analytics and others being added to the literature. In this paper, we evaluate the usefulness of a model using Rough Set Theory (RST) and Backpropagation Neural Network (BPNN) in effectively predicting the students’ overall performance. The dataset used consists of 10 different attributes and one decision factor belonging to 53 students collected from a language course which administers in-person education with the aid of an online platform for assignments. RST was implemented in order to reduce the number of attributes used as input in the neural network and the BPNN made an accurate prediction using only 5 of the initial attributes. Thus outperforming a model based solely on BPNN used on the original dataset and reducing computational costs.

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“…Odzaly et al [11] described the underlying risk management model in an Agile risk tool. An assessment model based on the combination of backpropagation neural network (BPNN) and rough set theory (RST) was put forward by Li et al [12]. A computational model for the reduction of the probability of project failure through the prediction of risks was proposed by Filippetto et al [13].…”

Section: Introductionmentioning

confidence: 99%

An Evidential Software Risk Evaluation Model

Deng

2022

Mathematics

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Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

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Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network

Cited by 18 publications

References 19 publications

Enhance Risks Management of Software Development Projects in Concurrent Multi-Projects Environment to Optimize Resources Allocation Decisions

Enhance Risks Management of Software Development Projects in Concurrent Multi-Projects Environment to Optimize Resources Allocation Decisions

Prediction of Student Performance Using Rough Set Theory And Backpropagation Neural Networks

An Evidential Software Risk Evaluation Model

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