Investigating the Impact of Functional Size Measurement on Predicting Software Enhancement Effort Using Correlation-Based Feature Selection Algorithm and SVR Method

Sakhrawi, Zaineb; Sellami, Asma; Bouassida, Nadia

doi:10.1007/978-3-030-64694-3_14

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Besides, COSMIC functional size is widely used as an estimation methodology for proper management. Researches in (Hussain et al, 2013;Sakhrawi et al, 2020;Salmanoglu et al, 2017) showed that despite other new measurements such as story points in agile methodologies, in all three examined cases COS-MIC based estimation was more precise and effective. The study (Fehlmann & Santillo, 2010) presented the comparisons between the impact of functional enhancement size generated from COSMIC and IFPUG (Popović & Bojić, 2012) methods on the effort prediction.…”

Section: Related Workmentioning

confidence: 88%

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

Ranković

Ivanović

et al. 2021

Connection Science

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This paper proposes a new, improved COmmon Software Measurement International Consortium function point (COSMIC FP) method that uses Artificial Neural Network (ANN) architectures based on Taguchi's Orthogonal Array to estimate software development effort. The minimum magnitude relative error (MRE) to evaluate these architectures considering the cost effect function, the type of data used in the training, testing, and validation of the proposed models, was used. Applying the fuzzification and clustering method to obtain seven different datasets, we would like to achieve excellent reliability and accuracy of the obtained results. Besides examining the influence of four input values, we aim to reduce the risks of potential errors, increase the coverage of a wide range of different projects and increase the efficiency and success of completing many various software projects. The main contributions of our work are as follows: the influence of four input values of the COSMIC FP method on the change of mean (MRE), development of two simple ANN architectures, the attainment of a small number of performed iterations in software effort estimation (less than 7), reduced software effort estimation time, the use of different values of the International Software Benchmarking Standards Group and other datasets used in the experiment.

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Section: Related Workmentioning

confidence: 88%

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

Ranković

Ivanović

et al. 2021

Connection Science

View full text Add to dashboard Cite

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Support vector regression for enhancement effort prediction of Scrum projects from COSMIC functional size

Sakhrawi

Sellami

Bouassida

2021

Innovations Syst Softw Eng

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Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping Study

2021

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Accurate prediction of software enhancement effort is a key success in software project management. To increase the accuracy of estimates, several proposals used machine-learning (ML) techniques for predicting the software project effort. However, there is no clear evidence for determining which techniques to select for predicting more accurate effort within the context of enhancement projects. This paper aims to present a systematic mapping study (SMS) related to the use of ML techniques for predicting software enhancement effort (SEME). A SMS was performed by reviewing relevant papers from 1995 through 2020. We followed well-known guidelines. We selected 30 relevant studies; 19 from journals and 11 conferences proceedings through 4 search engines. Some of the key findings indicate that (1) there is relatively little activity in the area of SEME, (2) most of the successful studies cited focused on regression problems for enhancement maintenance effort prediction, (3) SEME is the dependent variable the most commonly used in software enhancement project planning, and the enhancement size (or the functional change size) is the most used independent variables, (4) several private datasets were used in the selected studies, and there is a growing demand for the use of commonly published datasets, and (5) only single models were employed for SEME prediction. Results indicate that much more work is needed to develop repositories in all prediction models. Based on the findings obtained in this SMS, estimators should be aware that SEME using ML techniques as part of non-algorithmic models demonstrated increased accuracy prediction over the algorithmic models. The use of ML techniques generally provides a reasonable accuracy when using the enhancement functional size as independent variables.

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Investigating the Impact of Functional Size Measurement on Predicting Software Enhancement Effort Using Correlation-Based Feature Selection Algorithm and SVR Method

Cited by 4 publications

References 22 publications

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

Support vector regression for enhancement effort prediction of Scrum projects from COSMIC functional size

Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping Study

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