A Survey on Software Cost Estimation Techniques

Chirra, Sai Mohan Reddy; Reza, Hassan

doi:10.4236/jsea.2019.126014

Cited by 28 publications

(21 citation statements)

References 31 publications

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“…This is why COCOMO is used extensively by project managers [16]. The COCOMO is one of the best known traditional models of programmatic estimate theories and it was proposed by the scientist Boehm in 1981 [17] and it is also called COCOMO1 or COCOMO81 and it is a group of three typical levels: basic, Intermediate, and detailed. They all have a relationship between system size measured in KLOC and effort developed in person per month [18].…”

Section: The Cocomo Modelmentioning

confidence: 99%

Designing a Tool to Estimate Software Projects Based on The Swarm Intelligence

Khaleel¹

2021

IJIES

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Estimating models in software engineering are used to estimate some important and future characteristics of the software project, such as estimating the developed project effort, and that failure in the program is mainly due to wrong project management practices, so estimating the software effort is a very important step in the software management process for large projects. In this research, a software estimation tool was built to find an efficient and accurate method for estimating the effort. The average COnstructive COst MOdel COCOMO was used, which is classified as one of the best traditional methods among arithmetic estimation models. Four methods of swarm intelligence were used, the first of which is the Glowing worm Swarm Optimization GSO method and the second is the Bird Swarm Algorithm BSA and the third is the first proposed method hybrid BSA-GSO Method1 BSA-GSOM1, where the GSO and BSA algorithms were hybridized, and the performance of the third method was improved to form the fourth method, represented by the second hybrid new method, which called hybrid BSA-GSO Method2 BSA-GSOM2. The new tool was implemented with all its methods on the NASA data set and satisfactory results were obtained by the first and second swarms intelligence, and excellent results were obtained in the first proposed method, but the results of the second proposed method were better and more accurate than the previous ones. many measurements of performance were used for all the methods, the second proposed method yielded the best results from everyone.

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Section: The Cocomo Modelmentioning

confidence: 99%

Designing a Tool to Estimate Software Projects Based on The Swarm Intelligence

Khaleel¹

2021

IJIES

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“…Multiple studies reported that COCOMO-II supports a calibration process in an effective manner. In other words, it means that primary metrics used in COCOMO-II model are clearly defined, and the variables are presented in a more detailed manner [33]. Another noticeable advantage of using COCOMO-II is its ability to function more effectively due to dynamically adjusting according to the reported changes.…”

Section: Limitations Of Existing Cost Estimation Modelsmentioning

confidence: 99%

“…However, in the GSD context, the machine learning-based cost estimation models are only presented at a conceptual level [30]. Chirra et al [33] reported that when a model is created with different historical project datasets, there is a variation in the predicted accuracy. While using machine learning models in the GSD context, it is difficult to determine which technique will give more accurate results on which type of dataset.…”

Section: Limitations Of Existing Cost Estimation Modelsmentioning

confidence: 99%

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

Khan

et al. 2021

IEEE Access

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Global Software Development (GSD) projects comprise several critical cost drivers that affect the overall project cost and budget overhead. Thus, there is a need to amplify the existing model in GSD context to reduce the risks associated with cost overhead. Motivated by this, the current work aims at amplifying the existing algorithmic model with GSD cost drivers to get efficient estimates in the context of GSD. To achieve the targeted research objective, current state-of-the-art cost estimation techniques and GSD models are reported. Furthermore, the current study has proposed a conceptual framework to amplify the algorithmic COCOMO-II model in the GSD domain to accommodate additional cost drivers empirically validated by a systematic review and industrial practitioners. The main phases of amplification include identifying cost drivers, categorizing cost drivers, forming metrics, assignment of values, and finally altering the base model equation. Moreover, the proposed conceptual model's effectiveness is validated through expert judgment, case studies, and Magnitude of Relative Estimates (MRE). The obtained estimates are efficient, quantified, and cover additional GSD aspects than the existing models; hence we could overcome the GSD project's overall risk by implementing the model. Finally, the results indicate that the model needs further calibration and validation.

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“…A busca pela otimização dos parâmetros do COCOMO Básico tem gerado muitos trabalhos nos últimos anos (Chirra et al 2019). Modelo Semi-Destacado 50-300 KLOC E = 3.0 * (KLOC) 1.20 Modelo Embarcado Acima de 300 KLOC E = 3.6 * (KLOC) 1.12 Quadro 4.…”

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