An empirical validation of the relationship between the magnitude of relative error and project size

Stensrud, Erik; Foss, Tron; Kitchenham, Barbara; Myrtveit, Ingunn

doi:10.1109/metric.2002.1011320

Cited by 53 publications

(34 citation statements)

References 7 publications

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“…This is the most widely used evaluation criterion for assessing the performance of competing software effort estimation models [50], [51], [52]. Each individual project case's MRE value is a direct measure of the absolute difference between the prediction and the known actual value [53]. Therefore the smaller the MRE indicates the better the performance of the prediction system [54].…”

Section: Threats To Validitymentioning

confidence: 99%

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Kocagüneli

Menzies

Bener

et al. 2012

IIEEE Trans. Software Eng.

181

163

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Abstract-Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide the design of effort estimators. Method: We identified the essential assumption of analogy-based effort estimation: i.e. the immediate neighbors of a project offer stable conclusions about that project. We test that assumption by generating a binary tree of clusters of effort data and comparing the variance of super-trees vs smaller sub-trees. Results: For ten data sets (from Coc81, Nasa93, Desharnais, Albrecht, ISBSG, and data from Turkish companies), we found: (a) the estimation variance of cluster sub-trees is usually larger than that of cluster super-trees; (b) if analogy is restricted to the cluster trees with lower variance then effort estimates have a significantly lower error (measured using MRE and a Wilcoxon test, 95% confidence, compared to nearest-neighbor methods that use neighborhoods of a fixed size). Conclusion: Estimation by analogy can be significantly improved by a dynamic selection of nearest neighbors, using only the project data from regions with small variance.

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Section: Threats To Validitymentioning

confidence: 99%

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Kocagüneli

Menzies

Bener

et al. 2012

IIEEE Trans. Software Eng.

181

163

View full text Add to dashboard Cite

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“…On the other hand, there is a concern about MRE because MRE is biased [33] and not always reliable as a prediction accuracy measure [18]. However, MRE has been the de facto standard in the software effort prediction literature and no alternative standard exists at present.…”

Section: Prediction Accuracy Measuresmentioning

confidence: 99%

An application of Bayesian network for predicting object-oriented software maintainability

Koten

Gray

2006

Information and Software Technology

153

103

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The Department of Information Science is one of six departments that make up the School of Business at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in spatial information processing, connectionist-based information systems, software engineering and software development, information engineering and database, software metrics, distributed information systems, multimedia information systems and information systems security are particularly well supported.The views expressed in this paper are not necessarily those of the department as a whole. The accuracy of the information presented in this paper is the sole responsibility of the authors. CopyrightCopyright remains with the authors. Permission to copy for research or teaching purposes is granted on the condition that the authors and the Series are given due acknowledgment. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors. CorrespondenceThis paper represents work to date and may not necessarily form the basis for the authors' final conclusions relating to this topic. It is likely, however, that the paper will appear in some form in a journal or in conference proceedings in the near future. The authors would be pleased to receive correspondence in connection with any of the issues raised in this paper, or for subsequent publication details. Please write directly to the authors at the address provided below. (Details of final journal/conference publication venues for these papers are also provided on the Department's publications web pages: http://www.otago.ac.nz/informationscience/pubs/). Any other correspondence concerning the Series should be sent to the DPS Coordinator. AbstractAs the number of object-oriented software systems increases, it becomes more important for organizations to maintain those systems effectively. However, currently only a small number of maintainability prediction models are available for objectoriented systems. This paper presents a Bayesian network maintainability prediction model for an object-oriented software system. The model is constructed using object-oriented metric data in Li and Henry's datasets, which were collected from two different object-oriented systems. Prediction accuracy of the model is evaluated and compared with commonly used regression-based models. The results suggest that the Bayesian network model can predict maintainability more accurately than the regression-based models for one system, and almost as accurately as the best regression-based model for the other system.

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“…However, the accuracy of an estimation technique is proportional to Pred (25), Pred (25) and inversely proportional to the MMRE (Marza and Seyyedi, 2009;Martin et al, 2005). According to (Stensrud, 2003), MMRE is used for two kinds of assessments (at least). MMRE is to select between competing prediction models and to provide a quantitative measure of the uncertainty of a prediction.…”

Section: Evaluation Techniques Criteriamentioning

confidence: 99%

A Comparison of Stepwise and Fuzzy Multiple Regression Analysis Techniques for Managing Software Project Risks: Analysis Phase

Elzamly

Hussin

2014

Journal of Computer Science

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Risk is not always avoidable, but it is controllable. The aim of this study is to identify whether those techniques are effective in reducing software failure. This motivates the authors to continue the effort to enrich the managing software project risks with consider mining and quantitative approach with large data set. In this study, two new techniques are introduced namely stepwise multiple regression analysis and fuzzy multiple regression to manage the software risks. Two evaluation procedures such as MMRE and Pred (25) is used to compare the accuracy of techniques. The model's accuracy slightly improves in stepwise multiple regression rather than fuzzy multiple regression. This study will guide software managers to apply software risk management practices with real world software development organizations and verify the effectiveness of the new techniques and approaches on a software project. The study has been conducted on a group of software project using survey questionnaire. It is hope that this will enable software managers improve their decision to increase the probability of software project success.

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An empirical validation of the relationship between the magnitude of relative error and project size

Cited by 53 publications

References 7 publications

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

Exploiting the Essential Assumptions of Analogy-Based Effort Estimation

An application of Bayesian network for predicting object-oriented software maintainability

A Comparison of Stepwise and Fuzzy Multiple Regression Analysis Techniques for Managing Software Project Risks: Analysis Phase

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