A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons

Bardsiri, Vahid Khatibi; Jawawi, Dayang Norhayati Abang; Hashim, Siti Zaiton Mohd; Khatibi, Elham

doi:10.1007/s10664-013-9241-4

Cited by 50 publications

(27 citation statements)

References 53 publications

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“…To perform multiple linear regression analysis, the β-value (the regression coefficient) was used to verify if the independent variables had explanatory power (Park et al, 2020). In this work we used the stepwise mode of regression analysis, which is indicated when multiple independent variables are taken as predictors (Shepperd and MacDonell, 2012;Bardsiri et al, 2014). The stepwise regression technique aims to maximize the estimated power with a minimum number of independent variables.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015–2019

et al. 2021

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Continuous health care and the measurement of health-related physical fitness (HRPF) is necessary for prevention against chronic diseases; however, HRPF measurements including laboratory methods may not be practical for large populations owing to constraints such as time, cost, and the requirement for qualified technicians. This study aimed to develop a multiple linear regression model to estimate the HRPF of Korean adults, using easy-to-measure dependent variables, such as gender, age, body mass index, and percent body fat. The National Fitness Award datasets of South Korea were used in this analysis. The participants were aged 19–64 years, including 319,643 male and 147,600 females. HRPF included hand grip strength (HGS), flexibility (sit and reach), muscular endurance (sit-ups), and cardiorespiratory fitness (estimated VO2max). An estimation multiple linear regression model was developed using the stepwise technique. The outlier data in the multiple regression model was identified and removed when the absolute value of the studentized residual was ≥2. In the regression model, the coefficient of determination for HGS (adjusted R2: 0.870, P < 0.001), muscular endurance (adjusted R2: 0.751, P < 0.001), and cardiorespiratory fitness (adjusted R2: 0.885, P < 0.001) were significantly high. However, the coefficient of determination for flexibility was low (adjusted R2: 0.298, P < 0.001). Our findings suggest that easy-to-measure dependent variables can predict HGS, muscular endurance, and cardiorespiratory fitness in adults. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF.

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Section: Discussionmentioning

confidence: 99%

Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015–2019

et al. 2021

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“…The ATLM proposed by Whigham et al 10 is a baseline model in SEE developed using multiple linear regression. Whigham et al 10 compared ATLM to other complex models such as ensemble methods 4 and a hybrid approach (incorporating particle swarm optimization, analogy‐based estimation, and clustering 67 ).…”

Section: Methodsmentioning

confidence: 99%

A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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Context The use of a subset of recently completed and exemplary data, namely, Bellwether moving window (BMW) has proven successful to result in improved accuracy in software effort estimation (SEE). These outcomes were achieved based on the theory that estimation outcome of a future event depends on previous events. Thus, the existence of a BMW yield improved prediction accuracy for new project estimation. However, the conclusion instability problem across learners still threatens the reliability of SEE for new projects. Such instability concerns are attributed to the data subset considered for the training and validation needs of learners. Objective To investigate whether the use of BMWs together with an effort classification scheme can minimize the conclusion instability problem across learners. Method We apply a Bellwether method comprising of three operators, namely, SORT+CLUSTER, GENERATE_TPM, and APPLY to sample the BMW from a pool of chronological projects from the Maxwell and International Software Benchmarking Standards Group (ISBSG) datasets. The sampled BMW is benchmarked against the entire collection of preprocessed projects, namely, growing portfolio to evaluate prediction and classification accuracy across a set of learners–ElasticNet regression, deep neural networks, and automatically transformed linear model. Results (1) BMW exists in the studied projects and (2) training the learners with a BMW of average window size 28.5%–75.5% of the growing portfolio (not older than 3 years) relatively minimizes the conclusion instability of prediction results. Conclusion When BMWs are available, we recommend their use for estimating the effort for a new project to minimize the conclusion instability problem.

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“…Dataset segmentation omits inconsistent projects and improves estimation accuracy as was described by Bardisiri et al [7]. Both [17,18] present the usage of the particle swarm optimization algorithm.…”

Section: Related Workmentioning

confidence: 97%

Spectral Clustering Effect in Software Development Effort Estimation

2021

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Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.

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A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons

Cited by 50 publications

References 53 publications

Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015–2019

Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015–2019

A classification scheme to improve conclusion instability using Bellwether moving windows

Spectral Clustering Effect in Software Development Effort Estimation

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