Analyzing Effect of Ensemble Models on Multi-Layer Perceptron Network for Software Effort Estimation

Shukla, Suyash; Kumar, Sandeep; Bal, Pravas Ranjan

doi:10.1109/services.2019.00116

Cited by 11 publications

(10 citation statements)

References 7 publications

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“…These data sets are used to compare machine learning and, in particular, meta‐heuristic algorithms used for software development problems [25]. Albrecht [26], Desharnais [27], Kemerer [28], Maxwell [29] and Nasa [30] data sets.…”

Section: Resultsmentioning

confidence: 99%

Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

2020

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Section: Resultsmentioning

confidence: 99%

Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

2020

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“…However, one question is whether we can further improve the effort after the calculation of the counting process. Recent studies [19][20][21][22][23] show that combining an ensemble model and other approaches provides better results than using a single model. This study applies an ensemble model to the result after the counting and calculating effort to improve the accuracy gained from FP counting based on the proposed functional complexity weight.…”

Section: Problem Formulationmentioning

confidence: 99%

A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation

et al. 2022

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Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.

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“…However, there are some limitations of using them, as indicated by Shukla et al The ML-based methods do not work well on all datasets. Moreover, this paper recommends exploring the Neural Networks-based estimation method to improve estimation accuracy [10]. The use of Neural Networks in estimation is a wellpracticed approach as indicated by Nassif et al [25].…”

Section: Related Workmentioning

confidence: 99%

“…The revolution in the advancement of technology is taking place very rapidly and opening new directions to solve different problems. Besides, the researchers are strongly motivated to adopt advanced technologies to solve the trending effort estimation problem, such as Machine Learning (ML) based estimation methods are introduced to improve the accuracy of software effort estimation to encounter the shortcomings of existing techniques [10]. From the list of recent technological techniques, Software Engineering (SE) and Computer Network (CN) community has started to leverage the capabilities of blockchain to improve the performance.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Software Effort Estimation: An Empirical Study

et al. 2022

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Context: The success or failure of any software development project significantly depends on the accuracy of its effort estimates. Software development effort estimation is the foundation for project bidding, budgeting, planning, and cost control. Problem: The literature shows that a lot of work has been done on software effort estimation. But still, there is a need for improvement in effort estimation by introducing new methodologies. The structured group-based and analogy-based effort estimations are the widely used estimation methods. Nevertheless, there are several shortcomings of using these methods such as lack of experts, lack of historical data, and biasness in expert opinion, which negatively affect the estimation results. Motivation: With the advancement of technologies, such limitations could be overcome. Such as leveraging the applicability of blockchain in several domains such as improvement in software development process and network security. Method: In this article, we have proposed a Blockchain-Based Software Effort Estimation (BBSEE) methodology to improve the software effort estimation. We employ the proposed method using Web and blockchain technologies. Moreover, we also proposed an evaluation criteria to assess the efficacy of the proposed method in terms of Mean Magnitude of Relative Error (MMRE), Mean Absolute Error (MAE), and percentage of successful predictions falling (PRED (25)). Result: We performed several case studies and analyses expert opinion of 52 organizations to present the efficacy of the proposed method. Conclusion: We observe that BBSEE method outperforms than expert judgment and analogy-based effort estimation methodologies in terms of software effort estimation.

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Analyzing Effect of Ensemble Models on Multi-Layer Perceptron Network for Software Effort Estimation

Cited by 11 publications

References 7 publications

Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

Improved intrusion detection method for communication networks using association rule mining and artificial neural networks

A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation

Blockchain-Based Software Effort Estimation: An Empirical Study

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