Employing use cases to early estimate effort with simpler metrics

Robiolo, Gabriela; Orosco, Ricardo

doi:10.1007/s11334-007-0043-y

Cited by 42 publications

(45 citation statements)

References 15 publications

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“…Authors in (Diev, 2006) and (Anda et al, 2001) worked on adjustment factors, while others in (Anda et al, 2001) and (Arnold and Pedross, 1998) highlighted the discrepancies in designing use case models. Researchers in (Robiolo and Orosco, 2008), (Robiolo et al, 2009) and (Ochodek and Nawrocki, 2008) proposed different size metrics such as transactions, TTPoints and paths, while others (Periyasamy and Ghode, 2009;Wang et al, 2009;Schneider and Winters, 2001;Braz and Vergilio, 2006;Nassif et al, 2011a,b;Mohagheghi et al, 2005;Ochodek et al, 2011) went further to extend the UCP model by providing new complexity weights or by modifying the method used to predict effort.…”

Section: Related Workmentioning

confidence: 99%

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Nassif

Ho²,

Capretz

2013

Journal of Systems and Software

166

160

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tSoftware estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effort based on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects.

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Section: Related Workmentioning

confidence: 99%

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Nassif

Ho²,

Capretz

2013

Journal of Systems and Software

166

160

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“…The research work concluded that "use case point" method is perhaps the best available tool for effort estimation of software development projects and highlighted that generalized standardization of "use case point" method will be an important contribution to software industry. Robiolo, G. and Orosco R. (2008) [18] had given more stress on the versatility of use case point method which can be employed to estimate the effort in early stages of development. The research work was intended to find out an alternative method for estimating software effort which can give better performance when compared to function-point method of software effort estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

Use Case Point Method of Software Effort Estimation: A Review

Kirmani¹,

Wahid²

2015

IJCA

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Software project management is one of the critical activities in software development process. Many software development projects fail due to poor management of projects. The main aim of software project management team is to count what is countable, measure what is measurable and prepare measurable parameters for continuously monitor and manage a software development project. One of the very important factors in proper management of a software project is software estimation. At present most of software projects developed are using object oriented technology for the development of software projects. Researchers across the globe have used object oriented technology based method known as use case point method for estimating efforts for a software project development. This research work is an effort to review different invariants of use case point method of effort estimation. The research work also highlights the importance of continuously refining the UCP method in order to reduce the gap between the estimated and actual efforts.

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“…Unfortunately, only a few approaches used use cases to estimate the testing effort. Use cases have, however, been used in many studies to estimate the software development effort (cost) [e.g., 18,22,27,[29][30][31]. Nageshwaran [28] presents a use cases based approach for test effort estimation considering weight and environmental factors.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

Badri

Flageol

2013

Lecture Notes in Computer Science

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Abstract. Software testing plays a crucial role in software quality assurance.Software testing is, however, a time and resource consuming process. It is, therefore, important to estimate as soon as possible the effort required to test software. Unfortunately, little is known about the prediction of the testing effort. The study presented in this paper aims at exploring empirically the prediction of the testing effort from use cases. We address the testing effort from the perspective of test suites size. We used four metrics to characterize the size and complexity of use cases, and three metrics to quantify different perspectives of the size of corresponding test suites. We used the univariate logistic regression analysis to evaluate the individual effect of each use case metric on the size of test suites. The multivariate logistic regression analysis was used to explore the combined effect of the use case metrics. The performance of the prediction models was evaluated using receiver operating characteristic analysis. An experimental study, using data collected from five Java case studies, is reported providing evidence that some of the use case metrics are significant predictors of the size of test suites.

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Employing use cases to early estimate effort with simpler metrics

Cited by 42 publications

References 15 publications

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Towards an early software estimation using log-linear regression and a multilayer perceptron model

Use Case Point Method of Software Effort Estimation: A Review

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

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