Finding conclusion stability for selecting the best effort predictor in software effort estimation

Keung, Jacky; Kocagüneli, Ekrem; Menzies, Tim

doi:10.1007/s10515-012-0108-5

Cited by 69 publications

(94 citation statements)

References 28 publications

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“…Feature subset selection is one of the most influential factors determining the performance of an ABE framework [4], [28]. To avoid the issue of unstable results and to improve generalization of the results, we adopted 4 feature selection methods in this study, all of which are often adopted in software effort estimation literature [4], [13]:…”

Section: Four Feature Subset Selection Methodsmentioning

confidence: 99%

“…To avoid the issue of unstable results and to improve generalization of the results, we adopted 4 feature selection methods in this study, all of which are often adopted in software effort estimation literature [4], [13]:…”

Section: Four Feature Subset Selection Methodsmentioning

confidence: 99%

“…For the remaining 5 techniques in Table 1, UAVG is the fundamental technique used in the basic ABE framework [1], [4]. It aggregates the effort values selected from the k analogues using a simple unweighted mean.…”

Section: Solution Adaptation Techniques For Abementioning

confidence: 99%

“…All of these are available in the tera-PROMISE software engineering repository (Accessible through [18]), where the datasets are made available and commonly used in empirical software engineering studies. To maximize the validity of our study, these 19 datasets were taken from the list of 20 datasets which is the highest number of datasets that appeared in software development effort estimation literature [4], [13]. We decided to exclude one dataset, namely Telecom, because it does not contain any software size variables, which are required by the LSA, MSA, RTM and the proposed LSA-X techniques.…”

Section: Datasets and Their Prfactorsmentioning

confidence: 99%

“…It derives an estimated effort value from the total amount of effort used on already completed similar software projects, following a hypothesis: projects with similar characteristics will require similar amounts of effort to complete development [1], [2]. Numerous empirical studies such as [2]- [4] have reported that ABE is an excellent method in terms of accuracy, as well as being intuitively easy to understand.…”

Section: Introductionmentioning

confidence: 99%

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LSA-X: Exploiting Productivity Factors in Linear Size Adaptation for Analogy-Based Software Effort Estimation

Phannachitta

Monden

Keung

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYAnalogy-based software effort estimation has gained a considerable amount of attention in current research and practice. Its excellent estimation accuracy relies on its solution adaptation stage, where an effort estimate is produced from similar past projects. This study proposes a solution adaptation technique named LSA-X that introduces an approach to exploit the potential of productivity factors, i.e., project variables with a high correlation with software productivity, in the solution adaptation stage. The LSA-X technique tailors the exploitation of the productivity factors with a procedure based on the Linear Size Adaptation (LSA) technique. The results, based on 19 datasets show that in circumstances where a dataset exhibits a high correlation coefficient between productivity and a related factor (r ≥ 0.30), the proposed LSA-X technique statistically outperformed (95% confidence) the other 8 commonly used techniques compared in this study. In other circumstances, our results suggest using any linear adaptation technique based on software size to compensate for the limitations of the LSA-X technique.

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Section: Four Feature Subset Selection Methodsmentioning

confidence: 99%

Section: Four Feature Subset Selection Methodsmentioning

confidence: 99%

Section: Solution Adaptation Techniques For Abementioning

confidence: 99%

Section: Datasets and Their Prfactorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LSA-X: Exploiting Productivity Factors in Linear Size Adaptation for Analogy-Based Software Effort Estimation

Phannachitta

Monden

Keung

et al. 2016

IEICE Trans. Inf. & Syst.

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A new approach to the QS university ranking using the composite I‐distance indicator: Uncertainty and sensitivity analyses

Dobrota

Bulajić

Bornmann

et al. 2015

Asso for Info Science & Tech

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Some major concerns of universities are to provide quality in higher education and enhance global competitiveness, thus ensuring a high global rank and an excellent performance evaluation. This article examines the Quacquarelli Symonds (QS) World University Ranking methodology, pointing to a drawback of using subjective, possibly biased, weightings to build a composite indicator (QS scores). We propose an alternative approach to creating QS scores, which is referred to as the composite I-distance indicator (CIDI) methodology. The main contribution is the proposal of a composite indicator weights correction based on the CIDI methodology. It leads to the improved stability and reduced uncertainty of the QS ranking system. The CIDI methodology is also applicable to other university rankings by proposing a specific statistical approach to creating a composite indicator.

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Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Idri

Abnane

Abran

2017

J Software Evolu Process

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Software development effort estimation (SDEE) plays a primary role in software project management. But choosing the appropriate SDEE technique remains elusive for many project managers and researchers. Moreover, the choice of a reliable estimation accuracy measure is crucial because SDEE techniques behave differently given different accuracy measures. The most widely used accuracy measures in SDEE are those based on magnitude of relative error (MRE) such as mean/median MRE (MMRE/MedMRE) and prediction at level p (Pred(p)), which counts the number of observations where an SDEE technique gave MREs lower than p. However, MRE has proven to be an unreliable accuracy measure, favoring SDEE techniques that underestimate. Consequently, an unbiased measure called standardized accuracy (SA) has been proposed. This paper deals with the Pred(p) and SA measures. We investigate (1) the consistency of Pred(p) and SA as accuracy measures and SDEE technique selectors, and (2) the relationship between Pred(p) and SA. The results suggest that Pred(p) is less biased towards underestimates and generally selects the same best technique as SA. Moreover, SA and Pred(p) measure different aspects of technique performance, and SA may be used as a predictor of Pred(p) by means of the 3 association rules.

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Finding conclusion stability for selecting the best effort predictor in software effort estimation

Cited by 69 publications

References 28 publications

LSA-X: Exploiting Productivity Factors in Linear Size Adaptation for Analogy-Based Software Effort Estimation

LSA-X: Exploiting Productivity Factors in Linear Size Adaptation for Analogy-Based Software Effort Estimation

A new approach to the QS university ranking using the composite I‐distance indicator: Uncertainty and sensitivity analyses

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

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