An Amplified COCOMO-II Based Cost Estimation Model in Global Software Development Context

Khan, Junaid Ali; Khan, Saif Ur Rehman; Khan, Tamim Ahmed; Khan, Inayat Ur Rehman

doi:10.1109/access.2021.3089870

Cited by 17 publications

(5 citation statements)

References 36 publications

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“…Lamersdorf et al [25] introduced a hybrid model, i.e., the COBRA-based model, which amplified with cost overhead. The researches [27], [30], [33], [34], [36], [37], [39] also proposed algorithmic approaches, and most of the techniques are COCOMO II-based amplification for GSD. These methods are currently in the experimental stage and are not yet validated.…”

Section: Question 2 What Techniques Have Been Applied To Estimate The...mentioning

confidence: 99%

Systematic literature review on global software development based software cost estimation models and cost drivers

Ahmed,

Ibrahim,

Nasir

et al. 2023

IJEECS

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<p>Global software development (GSD) is a well - established discipline of software engineering that focuses on the advantage of a global environment. Effective cost estimation is critical for the success of GSD projects. Cost estimation in a GSD environment is a challenging task. As a re sult, GSD must emphasize cost estimation. Findings show that a number of researchers over the past few decades have emphasized GSD - based cost estimation in GSD; to the best of our knowledge, however, existing cost estimation have not taken into account man y GSD - based cost drivers that must be considered when estimating costs. Motivated by all this, the purpose of this study is to review the existing GSD - based cost estimation models/techniques and cost drivers that influence the accuracy of cost estimation. To identify and compile relevant research papers, a systematic literature review was carried out. From twenty - seven selected studies, initially, 86 GSD - based cost drivers and 12 GSD - based cost estimation models/techniques were extracted. After filtration, 26 cost drivers were identified as significant and to be considered in GSD - based cost estimation. This study significantly identifies GSD - based cost drivers and existing cost estimation techniques.</p>

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Section: Question 2 What Techniques Have Been Applied To Estimate The...mentioning

confidence: 99%

Systematic literature review on global software development based software cost estimation models and cost drivers

Ahmed,

Ibrahim,

Nasir

et al. 2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…These techniques not only address the challenges and risks associated with software cost estimation but their application impact project success in real-world scenarios, e.g. global software development [71], construction projects [55], defense industries [74], crosscompany database management [99], etc.…”

Section: A Rq-1: What Are the Most Common ML And Non-ml Techniques Fo...mentioning

confidence: 99%

Software Cost and Effort Estimation: Current Approaches and Future Trends

Rashid,

Shafi,

Ahmad

et al. 2023

IEEE Access

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Software cost and effort estimation is one of the most significant tasks in the area of software engineering. Research conducted in this field has been evolving with new techniques that necessitate periodic comparative analyses. Software project success largely depends on accurate software cost estimation as it gives an idea of the challenges and risks involved in the development. The great diversity of ML and Non-ML techniques has generated a comparison and progressed into the integration of these techniques. Based on varying advantages it has become imperative to work out preferred estimation techniques to improve the project development process. This study aims to present a systematic literature review (SLR) to investigate the trends of the articles published in the recent one and a half decades and to propose a way forward. This systematic literature review has proposed a three-stage approach to plan (Tollgate approach), conduct (Likert type scale), and report the results from five renowned digital libraries. For the selected 52 articles, artificial neural network model (ANN) and constructive cost model (COCOMO) based approaches have been the favored techniques. The mean magnitude of relative error (MMRE) has been the preferred accuracy metric, software engineering, and project management are the most relevant fields, and the promise repository has been identified as the widely accessed database. This review is likely to be of value for the development, cost, and effort estimations.

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“…To reduce cost risks, the authors of [22] amplified the constructive cost model (COCOMO-II) in the GSD context. The dataset was collected by conducting a questionnaire and receiving around 175 responses.…”

Section: B Software Risk Prediction Modelsmentioning

confidence: 99%

Risk Prediction Applied to Global Software Development using Machine Learning Methods

Hassan¹,

Abdel-Fattah²,

Ghoneim³

2022

IJACSA

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Software companies aim to develop high-quality software projects with the best global resources at the best cost. To achieve this global software development (GSD), an approach should be used which adopts work on projects across multiple distributed locations, and this is also known as distributed development. When companies attempt to implement GSD, they face numerous challenges owing to the nature of GSD and its differences from traditional methods. The objectives of this study were to identify the top software development factors that affect the overall success or failure of a software project using exploratory data analysis to find relationships between these factors, and to develop and compare risk prediction models that use machine learning classification techniques such as logistic regression, decision tree, random forest, support vector machine, K-nearest neighbors, and Naive Bayes. The findings of this study are as follows: in GSD, the top 18 factors influencing the software project are listed; and experiments show that the logistic regression and random forest models provide the best results, with an accuracy of 89% and 85%, respectively, and an area under the curve of 73% and 71%, respectively.

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An Amplified COCOMO-II Based Cost Estimation Model in Global Software Development Context

Cited by 17 publications

References 36 publications

Systematic literature review on global software development based software cost estimation models and cost drivers

Systematic literature review on global software development based software cost estimation models and cost drivers

Software Cost and Effort Estimation: Current Approaches and Future Trends

Risk Prediction Applied to Global Software Development using Machine Learning Methods

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