Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques

Idri, Ali; Amazal, Fatima Azzahra; Abran, Alain

doi:10.1002/int.21748

Cited by 27 publications

(42 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tables B2‐B7 of Appendix present the descriptions of their selected attributes, respectively. For the ISBSG R8 dataset, we conducted a data pre‐processing phase to select data (projects and attributes) in order to only retain data with high quality . This phase consists of two steps: Project selection: …”

Section: Design Of Empirical Evaluationmentioning

confidence: 99%

“…The purpose of this step is to select historical projects with high quality data. Therefore, we used the following four criteria as in: Data quality rating: This field contains an ISBSG rating code of A, B, C, or D applied to the project data by the ISBSG quality reviewers. This code denotes the soundness and integrity of the data of each project.…”

Section: Design Of Empirical Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Support vector regression‐based imputation in analogy‐based software development effort estimation

Idri

Abnane

Abran

2018

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

Missing data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort estimation (SDEE) techniques. To deal with this challenge, several imputation techniques have been investigated in SDEE and k‐nearest neighbors (KNN)‐based imputation is still the most frequently used. To the best of our knowledge, no study has used support vector regression (SVR)‐based imputation to construct accurate estimation techniques, in particular those based on analogy. This paper introduces a new imputation technique based on SVR for handling MD in two analogy‐based SDEE techniques: classical analogy and fuzzy analogy. More specifically, we investigate whether the use of SVR instead of KNN in imputing MD improves the predictive performance of these two analogy‐based techniques. A total of 1134 experiments were conducted involving seven datasets, SVR/KNN MD imputation techniques (KNN with Euclidean and Manhattan distances), three missingness mechanisms (missing completely at random, missing at random, non‐ignorable missing), and MD percentages from 10% to 90%. The results suggest that the use of SVR imputation, rather than KNN imputation, may improve the prediction performance of both analogy‐based techniques. Furthermore, we found that the impact of MD percentage upon effort prediction performance is reduced when using SVR rather than KNN. Moreover, fuzzy analogy generates better estimates in terms of the standardized accuracy measure than classical analogy regardless of the MD technique, the dataset used, the missingness mechanism, or the MD percentage.

show abstract

Section: Design Of Empirical Evaluationmentioning

confidence: 99%

Section: Design Of Empirical Evaluationmentioning

confidence: 99%

Support vector regression‐based imputation in analogy‐based software development effort estimation

Idri

Abnane

Abran

2018

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several performance measures have been used in SDEE to assess the accuracy of effort prediction techniques . The performance evaluation of SDEE techniques may vary depending on which accuracy measures are used, as shown by Myrtveit et al and Mittas et al This study investigated the Borda count method in order to combine a set of accuracy criteria so as to avoid weaknesses and take advantages of each criterion.…”

Section: Performance Measures Datasets Description and Effort Estimmentioning

confidence: 99%

“…Based on the systematic mapping study of El Koutbi et al, a large number of accuracy criteria have been used to assess the performance of effort variation approaches: Magnitude of relative error (MRE)‐based measures, Equation , are the most used (63% of selected papers); mean magnitude of relative error (MMRE), Equation , and Pred(25), Equation were used in 47% and 16% of selected papers, respectively

{AE}_{i} = (||, {Eact}_{i} - {Eest}_{i})

italicMRE = \frac{{italicAE}_{i}}{{italicEact}_{i}}

italicMMRE = \frac{1}{n} \sum_{i = 0}^{n} {MRE}_{i}

italicPred ((), 25) = \frac{100}{N} \sum_{i = 1}^{N} ({, \begin{matrix} 1 italicif {MRE}_{i} \leq 0.25 \\ 0 italicotherwise \end{matrix})

where Eact i and Eest i are the actual and estimated effort of the project P i respectively.…”

Section: Performance Measures Datasets Description and Effort Estimmentioning

confidence: 99%

“…The ISBSG repository release 8 used in this study contains more than 2000 software projects described by more than 50 numerical and categorical attributes. We conducted a data pre‐processing phase on select data (projects and attributes) of the ISBSG R8 dataset so as to retain only high quality data . This data pre‐processing phase consisted of two steps: Step 1: Project selection …”

Section: Performance Measures Datasets Description and Effort Estimmentioning

confidence: 99%

See 1 more Smart Citation

Empirical evaluation of an entropy‐based approach to estimation variation of software development effort

Koutbi

Idri

Abran

2018

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

As effort estimation has gained increasing attention, most of the techniques proposed have focused on the accuracy of effort estimates. Yet no clear conclusions have been drawn on which techniques perform best in all contexts. We propose an entropy‐based approach to effort estimate variation caused by measurement and model error sources whatever the effort estimation technique used. The proposed approach was empirically evaluated by exploring three entropy formulae, four interpolation methods, and two analogy‐based effort estimation approaches (crisp and fuzzy analogy) over seven datasets using the Jackknife evaluation method. The obtained results show that the three entropy formulae have in general the same positive influence on the performance of the entropy‐based approach measured in terms of absolute error of effort deviation. In addition, the spline interpolation outperformed all other interpolation methods, using any of the entropy formulae. Moreover, achievement percentages of the best variants of our approach closely approximated those of the Gaussian distribution confirming that the Gaussian distribution is useful for characterizing effort estimate variation.

show abstract

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Shahpar

Bardsiri

2021

Concurrency and Computation

View full text Add to dashboard Cite

Software development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy-based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global-local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well-known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

show abstract

Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques

Cited by 27 publications

References 37 publications

Support vector regression‐based imputation in analogy‐based software development effort estimation

Support vector regression‐based imputation in analogy‐based software development effort estimation

Empirical evaluation of an entropy‐based approach to estimation variation of software development effort

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Contact Info

Product

Resources

About