Improving the reliability of transaction identification in use cases

Ochodek, Mirosław; Alchimowicz, Bartosz; Jurkiewicz, J.; Nawrocki, Jerzy

doi:10.1016/j.infsof.2011.02.004

Cited by 28 publications

(13 citation statements)

References 17 publications

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“…Those activity types resemble types of actions identified by Ochodek et al [32] in their research that aimed at identifying transactions for the purpose of use-case-based effort estimation [33]. Five of the activity types (Update, Delete, Select, Validate, and Confirm) have their counterparts in Ochodek's types of actions (e.g.…”

Section: Related Workmentioning

confidence: 88%

Automated events identification in use cases

Jurkiewicz

Nawrocki

2015

Information and Software Technology

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Section: Related Workmentioning

confidence: 88%

Automated events identification in use cases

Jurkiewicz

Nawrocki

2015

Information and Software Technology

Self Cite

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“…Transactions in a use case scenario have been investigated as a method of tuning the traditional UCP concepts. Robiolo et al and Ochodek et al [ 7 , 10 ] introduce transaction-based methods. According to [ 7 ], a transaction is a part of a use case scenario that represents a stimulus.…”

Section: Methodsmentioning

confidence: 99%

Algorithmic Optimisation Method for Improving Use Case Points Estimation

2015

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This paper presents a new size estimation method that can be used to estimate size level for software engineering projects. The Algorithmic Optimisation Method is based on Use Case Points and on Multiple Least Square Regression. The method is derived into three phases. The first phase deals with calculation Use Case Points and correction coefficients values. Correction coefficients are obtained by using Multiple Least Square Regression. New project is estimated in the second and third phase. In the second phase Use Case Points parameters for new estimation are set up and in the third phase project estimation is performed. Final estimation is obtained by using newly developed estimation equation, which used two correction coefficients. The Algorithmic Optimisation Method performs approximately 43% better than the Use Case Points method, based on their magnitude of relative error score. All results were evaluated by standard approach: visual inspection, goodness of fit measure and statistical significance.

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“…Other studies attempted to develop predictive models based on UCP . On the other hand, many researchers attempted to improve the quality and accuracy of UCP model . In this paper, we mainly focus on effort estimation based upon UCP, so other studies that focus on UCP method improvements are ignored.…”

Section: Related Workmentioning

confidence: 99%

“…12,[15][16][17] On the other hand, many researchers attempted to improve the quality and accuracy of UCP model. 4,10,[18][19][20][21] In this paper, we mainly focus on effort estimation based upon UCP, so other studies that focus on UCP method improvements are ignored. In this regard, the most developed effort estimation models used fuzzy system based on the components of UCP.…”

Section: Related Workmentioning

confidence: 99%

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Project productivity evaluation in early software effort estimation

Azzeh

Nassif

2018

J Software Evolu Process

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The productivity factor has long been a key driver to estimate effort from Use Case Points (UCP) size measure, especially when historical dataset is absent. But, no one questions: Does productivity still matter when historical data are also available? To facilitate answering this question, the present paper studies the role of productivity from 2 perspectives. First, does learning productivity from historical data lead to better accuracy than using fixed productivity ratios? Second, what is the impact of ignoring productivity when estimating the effort from UCP? Five different models that use productivity factor have been used under different experimental settings and compared with some regression models that use only UCP size metrics. We found that dynamically learning and adjusting productivity from historical data are more efficient than using fixed productivity values. Moreover, using UCP size variables to estimate effort tends to be more accurate than using productivity and UCP variables. We also did not find any significant improvement when using UCP adjustment factors for measuring productivity. Finally, we conclude that the productivity factor is a good driver to generate effort estimate from UCP in the presence and absence of historical datasets. But using UCP size variables alone for predicting effort is more accurate than using productivity.

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Improving the reliability of transaction identification in use cases

Cited by 28 publications

References 17 publications

Automated events identification in use cases

Automated events identification in use cases

Algorithmic Optimisation Method for Improving Use Case Points Estimation

Project productivity evaluation in early software effort estimation

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