Estimates, uncertainty, and risk

Kitchenham, Barbara; Linkman, Stephen

doi:10.1109/52.589239

Cited by 92 publications

(68 citation statements)

References 2 publications

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“…However, it uses the conventional estimation model, and therefore it does not directly include the expert's knowledge in the model. Similar notion with our assumption that subjective judgment includes error is pointed out by Kitchenham et al [10]. They pointed out five sources of estimation uncertainty.…”

Section: Related Worksupporting

confidence: 51%

Incorporating Expert Judgment into Regression Models of Software Effort Estimation

Tsunoda

Monden

Keung

et al. 2012

2012 19th Asia-Pacific Software Engineering Conference

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Abstract-One of the common problems in building an effort estimation model is that not all the effort factors are suitable as predictor variables. As a supplement of missing information in estimation models, this paper explores the project manager's knowledge about the target project. We assume that the experts can judge the target project's productivity level based on his/her own expert knowledge about the project. We also assume that this judgment can be further improved, because using the expert's judgment solely could incur subjective perception. This paper proposes a regression model building/selection method to address this challenge. In the proposed method, a fit dataset for model building is divided into two or three subsets by project productivity, and an estimation model is built on each data subset. The expert judges the productivity level of the target project and selects one of the models to be used. In the experiment, we used three datasets to evaluate the produced effort estimation models. In the experiment, we adjusted the error rate of the judgment and analyzed the relationship between the error rate and the estimation accuracy. As a result, the judgment-incorporating models produced significantly higher estimation accuracy than the conventional linear regression model, where the expert's error rate is less than 37%.

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Section: Related Worksupporting

confidence: 51%

Incorporating Expert Judgment into Regression Models of Software Effort Estimation

Tsunoda

Monden

Keung

et al. 2012

2012 19th Asia-Pacific Software Engineering Conference

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“…It is widely acknowledged that the human-intensive nature of software development introduces uncertainty into the processes applied to create software-intensive products [Connor 2007, Kitchenham and Linkman 1997, Rao et al 2008, Yang et al 2007]. It is not generally possible to say exactly how many defects will be injected by an activity or exactly how much effort will be required.…”

Section: Model Limitationsmentioning

confidence: 99%

Modelling software processes

Kirk

MacDonell

Tempero

2009

Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications

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“…Kitchenham et al [13] refer to scope error as a risky situation to be avoided. In fact, scope error should be considered by project managers as a severe situation because, if we have never dealt with projects similar to the one being estimated, the predictive capabilities of the EM, calibrated on those projects, may be unpredictable and the risk of using the model for prediction may increase.…”

Section: Related Workmentioning

confidence: 99%

“…Kitchenham et al [13] refer to several sources of error, i.e. errors found in the measurements (Measurement error), produced by unsuitable mathematical models (model error), wrongly assumed input values (assumption error), or inadequacy of the projects chosen for building such estimates (scope error).…”

Section: Introductionmentioning

confidence: 99%