Software Cost Estimation Using Economic Production Models

Hu, Qing; Plant, Robert; Hertz, D.

doi:10.1080/07421222.1998.11518200

Cited by 36 publications

(9 citation statements)

References 21 publications

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“…According to evaluation suggested by Boehm [20], Intermediate model is proven to be better than the Basic model for effort prediction among the three levels. While the Detailed model is only slightly better than the Intermediate one [18] [24]. However, the 15 cost drivers of the Intermediate model are scalars ranging from 0.7 to 1.66 with a nominal value of one.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since the values of the cost drivers were originally from the software projects constructed during the period of the 1960s and 1970s. This model is still at a high level of uncertainty particularly in today's complex software environment regarding its accuracy, reliability and validity [24]. Therefore, there is a high possibility of leading to inaccurate effort estimate in such a complex software environment.…”

Section: Literature Reviewmentioning

confidence: 99%

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Factors on Software Effort Estimation

Kuan¹

2017

IJSEA

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Factors on Software Effort Estimation

Kuan¹

2017

IJSEA

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“…Ultimately, we use the embeddings to evaluate the actual cost (in terms of expended effort) Several other techniques exist for the estimation of cost in software development [5,9,10]. Briand et al discuss the merits of four different cost estimation techniques: ordinary least squares regression, stepwise ANOVA, CART, and analogy.…”

Section: Related Workmentioning

confidence: 99%

On evaluating the efficiency of software feature development using algebraic manifolds

Kothari

Bespalov

Mancoridis

et al. 2008

2008 IEEE International Conference on Software Maintenance

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Managers are often unable to explain objectively why or when effort was misplaced during the development process. In this paper, we present a formal technique to depict the expended effort during the life-cycle of a software feature using feature development manifolds (FDMs). Using the FDMs we can compute the preferred development path for a given feature. This development path includes the versions of a software feature that contributed to the final version of the feature in a positive way. The preferred development path excludes versions of the software feature that should have been skipped. Once the preferred development path is computed the amount of wasted effort can be quantified using the metric that we have developed. We demonstrate the effectiveness of our approach to compute wasted software feature development by applying our technique to two large open source software systems, Gaim and Firefox.

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“…The basic level effort is determined early in SDLC, while the intermediate and advanced levels are applied at later stages in SDLC. Depending on the application level, the overall COCOMO remains the same, except EAF is determined as follows: The effort estimations at the basic and advanced levels were not so accurate [20].…”

Section: Appendix Software Effort Estimation Models the Cart Algoritmentioning

confidence: 99%

A probabilistic model for predicting software development effort

Pendharkar

Subramanian

Rodger

2005

IIEEE Trans. Software Eng.

174

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Abstract-Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic models, when compared to traditional point forecast estimation models, is that Bayesian models provide tools for risk estimation and allow decision-makers to combine historical data with subjective expert estimates. In this paper, we use a Bayesian network model and illustrate how a belief updating procedure can be used to incorporate decision-making risks. We develop a causal model from the literature and, using a data set of 33 real-world software projects, we illustrate how decision-making risks can be incorporated in the Bayesian networks. We compare the predictive performance of the Bayesian model with popular nonparametric neural-network and regression tree forecasting models and show that the Bayesian model is a competitive model for forecasting software development effort.

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Software Cost Estimation Using Economic Production Models

Cited by 36 publications

References 21 publications

Factors on Software Effort Estimation

Factors on Software Effort Estimation

On evaluating the efficiency of software feature development using algebraic manifolds

A probabilistic model for predicting software development effort

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