A novel method for software effort estimation: Estimating with boundaries

Saraç, Ömer Faruk; Duru, N. Jeremi

doi:10.1109/inista.2013.6577643

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…8 Business Area Type (BAT) [45], [47], [53] 9 Primary Programming Language (PPL) [49], [52] 10 Application Group (AG) [36], [52] 11 1st Database System (1DB) [52], [55] 12 Used Methodology (UM) [52], [56] 13 Count Approach (CA) [47], [57] 14 Project Type (PT) [54] 15 Resources Level (RL) [56] 16 1st Operation System (1OS) [57] 17 K-Means Algorithm [37], [38], [39], [40], [41], [42], [43], [44]…”

Section: Nomentioning

confidence: 99%

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

et al. 2022

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Introduction:The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group's Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. Aim: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. Method: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. Results: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. Conclusion: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.

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Section: Nomentioning

confidence: 99%

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

et al. 2022

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“…Authors used an intermediate model which gives better accuracy than the other two models. A study conducted by [25] utilized a new approach uniting k-means algorithm to estimate the software cost through MRE results. The study [26] experimented with a Neural Network technique using a perceptron learning algorithm to evaluate the software cost based on the COCOMO model.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

Ranković

Ivanović

et al. 2021

IEEE Access

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In this paper, two different architectures of Artificial Neural Networks (ANN) are proposed as an efficient tool for predicting and estimating software effort. Artificial Neural Networks, as a branch of machine learning, are used in estimation because they tend towards fast learning and giving better and more accurate results. The search/optimization embraced here is motivated by the Taguchi method based on Orthogonal Arrays (an extraordinary set of Latin Squares), which demonstrated to be an effective apparatus in a robust design. This paper aims to minimize the magnitude relative error (MRE) in effort estimation by using Taguchi's Orthogonal Arrays, as well as to find the simplest possible architecture of an artificial Neural Network for optimized learning. A descending gradient (GA) criterion has also been introduced to know when to stop performing iterations. Given the importance of estimating software projects, our work aims to cover as many different values of actual efficiency of a wide range of projects as possible by division into clusters and a certain coding method, in addition to the mentioned tools. In this way, the risk of error estimation can be reduced, to increase the rate of completed software projects.

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Toward Applying Agglomerative Hierarchical Clustering in Improving the Software Development Effort Estimation

Hai

Nhung

Jašek

2022

Software Engineering Perspectives in Systems

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A novel method for software effort estimation: Estimating with boundaries

Cited by 6 publications

References 9 publications

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

A New Approach to Software Effort Estimation Using Different Artificial Neural Network Architectures and Taguchi Orthogonal Arrays

Toward Applying Agglomerative Hierarchical Clustering in Improving the Software Development Effort Estimation

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