Classification of Effort Estimation Methods

Trendowicz, Adam; Jeffery, Ross

doi:10.1007/978-3-319-03629-8_6

Cited by 4 publications

(3 citation statements)

References 17 publications

(1 reference statement)

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“…The following studies can be mentioned as relevant examples of this trend: fuzzy theory dealing with uncertainty [26,27]; the Bayesian network improving PERT [28]; optimization of COCOMO parameters with the Genetic algorithm [29]; improving prediction values of UCP with the random forest technique [30]. Overviews of usage of machine learning in the software project estimation area can also be found in [31,32].…”

Section: Literature Reviewmentioning

confidence: 99%

Method of Software Development Project Duration Estimation for Scrum Teams with Differentiated Specializations

Teslyuk

Batyuk

Voityshyn

2022

Systems

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Estimation is an essential step of software development project planning that has a significant impact on project success—underestimation often leads to problems with the delivery or even causes project failure. An important aspect that the classical estimation methods are usually missing is the Agile nature of development processes in the implementation phase. The estimation method proposed in this article aims at software development projects implemented by Scrum teams with differentiated specializations. The method is based on the authors’ system of working-time balance equations and the approach to measuring project scope with time-based units—normalized development estimates. In order to reduce efforts spent on the estimation itself, an analysis of dependencies among project tasks is not mandatory. The outputs of the methods are not recommended to be treated as commitments; instead, they are supposed to be used to inform project stakeholders about the forecasted duration of a potential project. The method is simple enough to allow even an inexpensive spreadsheet-based implementation.

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Section: Literature Reviewmentioning

confidence: 99%

Method of Software Development Project Duration Estimation for Scrum Teams with Differentiated Specializations

Teslyuk

Batyuk

Voityshyn

2022

Systems

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“…Effort estimation techniques developed from past several year were categorised by Trendowicz and Jeffery categorised them into three groups: Data drive, Expert based and Hybrid [9]. Chulani, S. et al, categorised them into two groups: Algorithmic and Non-algorithmic models [10].…”

Section: Effort Estimationmentioning

confidence: 99%

Effort Estimation Techniques for Web Application Development: A Review

Saif

2017

ijarcs

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In present era of information technology web applications have become an indispensable and most pivotal source to disseminate information across geographically distant nodes. Most of the organisation public or private have switched their existence from traditional software based systems to web based software systems. Many characteristic differences between traditional software and web based software application made web based existence more popular with wide range adoption. The sudden surge in web application usability across different organisation have posed a grave challenge for web development companies to deliver their products onetime and within budget with much expected accurately, efficiency and reliability. Effort estimation is main domain that decides the budgetary constraints associated with web application development. Accurate estimates are very essential for both developer and client. Accurate estimates make web development companies to maintain their reputation, competitive edge and client base. Inaccuracy in effort estimates results to lose revenue, client base and competitive strength. Effort estimation is very import for every development organisation to estimate the development work in early stages of development to prove efficient, accurate and much reliable budget estimates. This paper is an effort to explore various effort estimation techniques for web development, their challenges and measures to predict accurate efforts.

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“…Los métodos y modelos de estimación de esfuerzo son muy variados y se clasifican de diversas formas según los autores que se consulte 3 . Por ejemplo, según [49] éstos se clasifican en a) "orientados a los datos" (data-driven) y b) basados en la opinión del experto. Los modelos "orientados a los datos" se subclasifican en "propietarios" y "no propietarios"; éstos últimos a su vez se subdividen entre los que están "basados en modelos" y aquellos que son "basados en analogía".…”

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