A Systematic Review of Software Development Cost Estimation Studies

Jørgensen, Henrik L.; Shepperd, Martin

doi:10.1109/tse.2007.256943

Cited by 726 publications

(564 citation statements)

References 279 publications

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“…There has been much work on SEE in the software engineering literature (Jørgensen and Shepperd 2007;Kitchenham et al 2007). Algorithmic SEE models have been studied for many years (Boehm 1981;Jørgensen and Shepperd 2007).…”

Section: For See Assuming No Chronologymentioning

confidence: 99%

“…Algorithmic SEE models have been studied for many years (Boehm 1981;Jørgensen and Shepperd 2007). Among them, ML algorithms have been increasingly investigated as automated SEE approaches (Jørgensen and Shepperd 2007).…”

Section: For See Assuming No Chronologymentioning

confidence: 99%

“…Software effort is typically the main cost driver in software projects (Jørgensen and Shepperd 2007;Stutzke 2006). Both over and underestimations of effort can cause problems to a company.…”

Section: Introductionmentioning

confidence: 99%

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Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Minku

Yao

2016

Autom Softw Eng

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Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by A preliminary version of this work appeared as Minku and Yao (2012a

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Section: For See Assuming No Chronologymentioning

confidence: 99%

Section: For See Assuming No Chronologymentioning

confidence: 99%

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Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Minku

Yao

2016

Autom Softw Eng

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“…The intention of this model was to provide an approach to deal with software engineering problems from the outlook of human economics as well as from the programming perception. Software engineering world has in the interim changed creating a need to transform the innovative COCOMO model [4] to the COCOMO II which was published in 2000. Which is widely accepted public cost model.…”

Section: Parametric Modelsmentioning

confidence: 99%

“…The SLIM tool is the product of SLIM (for the proprietary of Putnam"s model) which is a metrics-based estimation tool [4], developed by Quantitative Software Management (QSM), using validated data of over 2600 projects. These projects were classified into nine different application categories.…”

Section: B Slimmentioning

confidence: 99%

Parametric Models for Effort Estimation for Global Software Development

Ramacharan¹,

Rao²

2013

LNSE

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Abstract-The main objective & aim of this paper is to identify the problems that arise due to distributed software & offshore software development. This includes numerous critical success factors which may make vulnerable to the success of development. Generally for software development perfect estimation is crucial especially in globally distributed projects & offshore projects. This article introduces a Study based on COCOMO II, SLIM, ISBSG effort estimation methods. These methods enable more precise estimation methods than conventional methods.Index Terms-COCOMO II, effort estimation methods, SLIM, ISBSG, off shoring, risk management.

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Software Effort Prediction

Lim

Goel

2008

Wiley Encyclopedia of Computer Science and Engineering

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The development of effort models has been an area of considerable research in software engineering. Most early models were algorithmic and derived relationships empirically between effort and project characteristics, mostly using regression analysis. Recently, they have been complemented by machine learning and by neural net techniques. Various comparative studies have also been pursued to evaluate the predictive performance of different approaches. This article first highlights the main effort‐prediction techniques and comparative studies and then discusses the model development process and some relevant issues. An illustrative example is employed to develop support vector prediction models for data from several industrial projects.

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A Systematic Review of Software Development Cost Estimation Studies

Cited by 726 publications

References 279 publications

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Parametric Models for Effort Estimation for Global Software Development

Software Effort Prediction

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