Systematic Literature Review of Identifying Issues in Software Cost Estimation Techniques

Saleem, Muhammad Asif; Ahmad, R. Badlishah; Alyas, Tahir; Idrees, Muhammad; Asfandayar,; Farooq, Asif; Shahid, Adnan; Ali, Kahawaja

doi:10.14569/ijacsa.2019.0100844

Cited by 6 publications

(2 citation statements)

References 24 publications

(30 reference statements)

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“…The model works on simulation and emulates how the final software will appear. The model has inputs and outputs (Saleem et al, 2019). The inputs are the size of the project, the personnel needed for the development, the environmental characteristics, the constraints of the project, and the software project's complexity level.…”

Section: Seer-sem Modelmentioning

confidence: 99%

A Model for Total Cost Determination in Open-Source Software Ownership: Case of Kenyan Universities’ Learning Management System

Kodhek,

Kamau,

Musyoka

2024

CIS

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The adoption of open-source products is slowly increasing; the increase, however, is slower than expected, considering that most open-source products are freely available. Researchers and scholars have attributed the adoption levels to, among other things, a lack of know-how of the total cost of ownership of the open-source software. Thus, it is crucial for the cost of owning the software to be developed. While an ongoing endeavor to develop a model to determine the total cost of ownership of open-source software, the models have proved to be less accurate and do not capture essential elements. Moreover, there has been a rising call for organizations to adopt open-source software to lower the software costs incurred on proprietary software. If the cost of owning open-source software were known, it would be beneficial as several organizations and institutions could adopt it readily. The data was collected from Universities in Kiambu and Embu Counties. Linear regression analysis was done to help develop the model, and a mathematical model was developed. The proposed model was: total cost of open-source software ownership = direct + +indirect + hidden costs. To validate the model, it was subjected to expert validation. The model will be an outstanding contribution to information technology as it will make it possible to come up with the total cost of owning open-source software.

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Section: Seer-sem Modelmentioning

confidence: 99%

A Model for Total Cost Determination in Open-Source Software Ownership: Case of Kenyan Universities’ Learning Management System

Kodhek,

Kamau,

Musyoka

2024

CIS

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“…iii) Most used accuracy measures are MMRE, MdMRE, PRED. [63] Discussed issues of estimating cost of software projects.…”

Section: Research Paper Findingsmentioning

confidence: 99%

Evolution of Software Development Effort and Cost Estimation Techniques: Five Decades Study Using Automated Text Mining Approach

Jadhav

Kaur

Akter

2022

Mathematical Problems in Engineering

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Software development effort and cost estimation (SDECE) is one of the most important tasks in the field of software engineering. A large number of research papers have been published on this topic in the last five decades. Investigating research trends using a systematic literature review when such a large number of research papers are published is a very tedious and time-consuming task. Therefore, in this research paper, we propose a generic automated text mining framework to investigate research trends by analyzing the title, author’s keywords, and abstract of the research papers. The proposed framework is used to investigate research trends by analyzing the title, keywords, and abstract of select 1015 research papers published on SDECE in the last five decades. We have identified the most popular SDECE techniques in each decade to understand how SDECE has evolved in the past five decades. It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis. The NASA and ISBSG are the most used dataset for SDECE. The MMRE, MRE, and PRED are the most used accuracy measures for SDECE. Results of the proposed framework are validated by comparing it with the outcome of the previously published review work and we found that the results are consistent. We have also carried out a detailed bibliometric analysis and metareview of the review and survey papers published on SDECE. This research study is significant for the development of new models for cost and effort estimations.

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Application of machine learning techniques for cost estimation of engineer to order products

Rapaccini

Cadonna

Leoni

et al. 2022

International Journal of Production Research

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Systematic Literature Review of Identifying Issues in Software Cost Estimation Techniques

Cited by 6 publications

References 24 publications

A Model for Total Cost Determination in Open-Source Software Ownership: Case of Kenyan Universities’ Learning Management System

A Model for Total Cost Determination in Open-Source Software Ownership: Case of Kenyan Universities’ Learning Management System

Evolution of Software Development Effort and Cost Estimation Techniques: Five Decades Study Using Automated Text Mining Approach

Application of machine learning techniques for cost estimation of engineer to order products

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