ANN-Cuckoo Optimization Technique to Predict Software Cost Estimation

Desai, Vishnu Sai; Mohanty, Ramakanta

doi:10.1109/infocomtech.2018.8722380

Cited by 13 publications

(9 citation statements)

References 17 publications

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“…The authors of [92,93], regarding the application of a software development team's ANN-driven and optimized programs to recognize capacity gaps and prepare the planning and scheduling of SPM skills, facilitate the predicting and anticipation of talent demands based on applied intelligence technologies, articulating staff development resources and techniques. One of the key aims of [94] is to help forecast SCE by utilizing the current ANN learning process. The effects are root average and median proportional magnitude of the error.…”

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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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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“…2) The results obtained from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in the literature for software effort estimation. We selected five widely used nature-inspired algorithms (BAT [29,45], Cuckoo Optimization (CO) [35,53,54], Genetic Algorithm (GA) [22,30,33] and Ant Colony Optimization (ACO) [24,32], Particle Swarm Optimization (PSO) [27,34,46]) for comparison. In this work, for comparison analysis nature-inspired meta-heuristics algorithms are selected based on inspiration from: (i) Natural biological system (GA, SB), (ii) Theory of evolution (PSO), (iii) Insects activities (ACO), (iv) Group behavior of animals, and birds (GWO, CO, BAT).To validate the performances of these seven algorithms, a set of nine benchmark functions having wide dimensions is applied.…”

Section: B Contributionsmentioning

confidence: 99%

“…ANN is associated with several challenges including falling in local minima, and overfitting. Therefore, many researchers recommend using nature-inspired meta-heuristic algorithms for the following: (i) ANN training [46,53], (ii) feature selection in classification [52], and (iii) weights selection [47,49]. Wani et al, [46] used functional link ANN model for software effort estimation.…”

Section: State Of the Artmentioning

confidence: 99%

“…But the increase in the number of instances (beyond 50), resulted in an increase in execution time. In [53] ANN model is trained using the CO algorithm to predict software cost estimation.…”

Section: State Of the Artmentioning

confidence: 99%

“…3) Cuckoo Optimization Cuckoo Search (CS) algorithm [35,53,54] was proposed by Xinshe Yang and Suash Deb in 2009. This optimization method is based on brood parasitism of cuckoo along with aspects of Levy Flight.…”

Section: ) Grey Wolf Algorithmmentioning

confidence: 99%

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Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

et al. 2021

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Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

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A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

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Vieira

Bravo

et al. 2021

J Software Evolu Process

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Deep learning has recently experienced explosive growth in use, largely due to advances in neural networks and the availability of large corpora of domain data. Project management activities generate and handle large volumes of data. Software engineering closely relates to project management, so software engineering projects must be prone to the use of neural networks. We seek to obtain an accurate vision of how neural networks are being used in software engineering projects through a systematic mapping study. We confirm that neural networks have already made their way into these projects; however, we show that their current uses are limited to certain repetitive and legacy tasks. Given uncovered ample room for expansion, we point out a few directions the industry and academy can lean toward to in the next years for taking better advantage of neural networks in software engineering projects and immediately advancing the field. We investigate if, how, and to what extent have neural networks been employed to the advancement of software engineering projects. As such, a systematic mapping study was conducted, which led to the conclusion that even though these algorithms have indeed been employed on several software engineering tasks, this employment so far has been shy, mostly relying on legacy types of neural networks. More modern variants, namely, deep learning algorithms, are slowly gaining momentum and should be the trend going forward.

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ANN-Cuckoo Optimization Technique to Predict Software Cost Estimation

Cited by 13 publications

References 17 publications

Software Project Management Using Machine Learning Technique—A Review

Software Project Management Using Machine Learning Technique—A Review

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

A systematic mapping study on the employment of neural networks on software engineering projects: Where to go next?

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