Development Cycle Modeling: Resource Estimation

Denard, Samuel; Ertaş, A.; Mengel, Susan; Ekwaro-Osire, Stephen

doi:10.3390/app10145013

Cited by 5 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A “correct” decision sequence will produce a desired end product (DEP). SAbMDE enables calculation of project properties including effort estimates (Denard et al., 2020a) and process risk (Denard et al., 2020b).…”

Section: Prospective Applicationsmentioning

confidence: 99%

Lessons identified from applications of the Risk Analysis Quality Test Release 1.0

Waller,

Coleman,

Denard

et al. 2024

Risk Analysis

Self Cite

View full text Add to dashboard Cite

The Risk Analysis Quality Test Release 1.0 (RAQT1.0) was developed as a framework to encourage mutual understanding between technical risk analysts and risk management decision makers of risk assessment quality indicators. The initial version (release 1.0) was published by the Society for Risk Analysis (SRA) in 2020 with the intent of learning from early test applications whether the approach was useful and whether changes in approach or contents would be helpful. The results of applications across three diverse fields are reported here. The applications include both retrospective evaluations of past risk assessments and prospective guidance on the design of future risk assessment projects or systems. The fields represented include Quantitative Microbial Risk Assessment, Cultural Property Risk Analysis, and Software Development Cyber Risk Analysis. The RAQT1.0 proved helpful for identifying shortcomings in all applications. Ways in which the RAQT1.0 might be improved are also identified.

show abstract

Section: Prospective Applicationsmentioning

confidence: 99%

Lessons identified from applications of the Risk Analysis Quality Test Release 1.0

Waller,

Coleman,

Denard

et al. 2024

Risk Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…[2]. Estimation identifies the size, as well as the amount of time, effort of human skill, money, [3] and resources [4] required to build a system or product. Though several models have been developed for the past two decades, effort estimation remains a challenging tasks, as there are more uncertain and unstable data during the initial stages of software development.…”

Section: Inception Phase Requirement Phase Design Phase Construction ...mentioning

confidence: 99%

Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

G¹,

Varadarajan

2021

Electronics

View full text Add to dashboard Cite

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

show abstract

“…Since it has a significant impact role in the achievement of software projects, among other factors. A low estimate leads to schedule/budget delay and performance agreement, whereas an overestimation wastes resources [29]. A rising amount of research is being conducted to increase the estimation accuracy of the development of software.…”

Section: Introductionmentioning

confidence: 99%

Software Effort Estimation using Ensemble Learning

Oshaibi M,

AlKhanafseh,

Surakhi

2024

Preprint

View full text Add to dashboard Cite

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.

show abstract

Development Cycle Modeling: Resource Estimation

Cited by 5 publications

References 17 publications

Lessons identified from applications of the Risk Analysis Quality Test Release 1.0

Lessons identified from applications of the Risk Analysis Quality Test Release 1.0

Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Software Effort Estimation using Ensemble Learning

Contact Info

Product

Resources

About