Risk Prediction by Using Artificial Neural Network in Global Software Development

Iftikhar, Asim; Alam, Muhammad Mansoor; Ahmed, Rizwan; Musa, Shahrulniza; Su’ud, Mazliham Mohd

doi:10.1155/2021/2922728

Cited by 10 publications

(10 citation statements)

References 45 publications

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“…CloudSim proposed in 2009 [46]. CloudSim's multifaceted [27] software [67] framework scheme and structural components are shown in Figure 5. The CloudSim simulation layer enables the development and simulation of virtual computers, memory, storage, and bandwidth in virtualized cloud-based [27] data center [38] environments with specific organizational boundaries.…”

Section: A Cloudsim Architecturementioning

confidence: 99%

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Shahid¹,

Faiz²,

Alam³

et al. 2022

JISR-C

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Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

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Section: A Cloudsim Architecturementioning

confidence: 99%

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Shahid¹,

Faiz²,

Alam³

et al. 2022

JISR-C

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“…In [9], artificial neural network (ANN) model was created to predict the risk factors in GSD. The model used algorithms such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient.…”

Section: B Software Risk Prediction Modelsmentioning

confidence: 99%

“…For classification evaluation, the AUC is more accurate than the accuracy metric, although the computational cost is high compared to the accuracy metric [25]. The AUC metric equation can be expressed as follows: 𝑨𝑼𝑪 = 𝑺 𝑷 − 𝑻 𝒑 (𝑻 𝒏 + 𝟏 ) / 𝟐 𝑻 𝒑 𝑻 𝒏 (9) where 𝐒 𝑷 is the summation of all the positive examples, 𝐓 𝑷 is the number of positive examples, and 𝐓 𝒏 is the number of negative examples. In this section, the results of the software risk prediction model were discussed using six classification machine learning algorithms: logistic regression, DT, RF, SVM, (KNN), and naïve Bayes, and by using the dataset of 140 software projects in the real industry of global software development.…”

Section: ) Confusion Matrixmentioning

confidence: 99%

“…In addition, the challenges and limitations that have a significant effect on GSD should be pointed out. For example, it can be difficult for distributed teams to communicate with each other and work together due to language barriers, cultural norms and limits, time zones, leadership, team capabilities, and project management [9]- [11]. One of the most serious issues confronting GSD is the location, distance, and communication between the distributed teams [12].…”

Section: Introductionmentioning

confidence: 99%

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Risk Prediction Applied to Global Software Development using Machine Learning Methods

Hassan¹,

Abdel-Fattah²,

Ghoneim³

2022

IJACSA

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Software companies aim to develop high-quality software projects with the best global resources at the best cost. To achieve this global software development (GSD), an approach should be used which adopts work on projects across multiple distributed locations, and this is also known as distributed development. When companies attempt to implement GSD, they face numerous challenges owing to the nature of GSD and its differences from traditional methods. The objectives of this study were to identify the top software development factors that affect the overall success or failure of a software project using exploratory data analysis to find relationships between these factors, and to develop and compare risk prediction models that use machine learning classification techniques such as logistic regression, decision tree, random forest, support vector machine, K-nearest neighbors, and Naive Bayes. The findings of this study are as follows: in GSD, the top 18 factors influencing the software project are listed; and experiments show that the logistic regression and random forest models provide the best results, with an accuracy of 89% and 85%, respectively, and an area under the curve of 73% and 71%, respectively.

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“…For this purpose, we have employed modified firefly algorithm (MFA). Firefly algorithm (FA) is a machine learning (ML) technique which is getting popular these days due to their ability to deal with unstructured data [ 27 ]. Simple firefly algorithm (FA) does not provide any way to validate its results i.e., fitness scores.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm

Iftikhar

Ali

Alam

et al. 2022

Computational Intelligence and Neuroscience

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In today’s competitive world, software organizations are moving towards global software development (GSD). This became even more significant in times such as COVID-19 pandemic, where team members residing in different geographical locations and from different cultures had to work from home to carry on their tasks and responsibilities as travelling was restricted. These teams are distributed in nature and work on the same set of goals and objectives. Some of the key challenges which software practitioners face in GSD environment are cultural differences, communication issues, use of different software models, temporal and spatial distance, and risk factors. Risks can be considered as a biggest challenge of other challenges, but not many researchers have addressed risks related to time, cost, and resources. In this research paper, a comprehensive analysis of software project risk factors in GSD environment has been performed. Based on the literature review, 54 risk factors were identified in the context of software development. These were further classified by practitioners into three dimensions, i.e., time, cost, and resource. A Pareto analysis has been performed to discover the most important risk factors, which could have bad impact on software projects. Furthermore, a modified firefly algorithm has been designed and implemented to evaluate and prioritize the pertinent risk factors obtained after the Pareto analysis. All important risks have been prioritized according to the fitness values of individual risks. The top three risks are “failure to provide resources,” “cultural differences of participants,” and “inadequately trained development team members.”

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Risk Prediction by Using Artificial Neural Network in Global Software Development

Cited by 10 publications

References 45 publications

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Risk Prediction Applied to Global Software Development using Machine Learning Methods

Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm

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