More Accurate Cost Estimation for Internet of Things Projects by Adaptation of Use Case Points Methodology

Šilhavý, Radek; Bureš, Miroslav; Alipio, Melchizedek I.; Silhavy, Petr

doi:10.1109/jiot.2023.3281614

Cited by 3 publications

(4 citation statements)

References 63 publications

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“…They also presented an improved iteration of the initial iUCP effort estimation formula [16]. Introducing "UCP for IoT" or adapting the use case points method to estimate the size and effort for IoT systems was presented in [17]. It was validated with a case study of three IoT systems, demonstrating applicability and highlighting the need for further improvement and data collection in future work.…”

Section: Related Workmentioning

confidence: 99%

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Rankovic,

Rankovic

2024

JTAER

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Meeting customer requirements in software project management, even for large digital enterprises, proves challenging due to unpredictable human factors. It involves meticulous planning and environmental factor analysis, ultimately benefiting both companies and customers. This paper came as a natural extension of our previous work where we left ourselves curious about what impact environmental complexity factors (ECFs) have in a use case point (UCP) approach. Additionally, we wanted to possibly decrease the mean magnitude relative error (MMRE) with deep learning models such as long-short-term-memory (LSTM) and gradient recurrent unit (GRU). The data augmentation technique was used to artificially increase the number of projects, since in the industry world, digital enterprises are not keen to share their data. The LSTM model outperformed the GRU and XGBoost models, while the average MMRE in all phases of the experiment for all models achieved 4.8%. Moreover, the post-agnostic models showed the overall and individual impact of eight ECFs, where the third ECF “team experience” on a new project has been shown as the most influential one. Finally, it is important to emphasize that effectively managing human factors within ECFs in UCPs can have a significant impact on the successful completion of a project.

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Section: Related Workmentioning

confidence: 99%

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Rankovic,

Rankovic

2024

JTAER

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“…The incorporation of models can be also used in estimating the effort and scope. The use of models is more prevalent in development practices employing a more robust documentation, such as COSMIC [20,21], Use Case Points [30,31], or Function Points [32]. COSMIC can be used to derive functional size measures from UML software artifacts, such as Use case model, Activity, Sequence, Class, Package, and Component diagrams [20].…”

Section: Related Workmentioning

confidence: 99%

“…The Use Case Points-based method Know-UCP [30] utilizes the UML Use case model as a basis for deriving Use Case Points from UML diagrams to estimate the size of the software. Use Case Points can even be adapted to the development of IoT systems [31]. Function Points are another metric used to determine the size of software, and there exist approaches based on Function Points, such as [32], which heavily rely on modeling.…”

Section: Related Workmentioning

confidence: 99%

Compiling Requirements from Models for Early Phase Scope Estimation in Agile Software Development Projects

Bisikirskienė,

Čeponienė,

Jurgelaitis

et al. 2023

Applied Sciences

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Inadequate early scope estimation is a common problem in software projects, leading to failures in meeting project requirements. Agile projects usually do not concentrate on a comprehensive requirements analysis and specification before the start of the project, making scope assessment difficult. This paper presents the methodology for facilitating a more accurate early estimation of project scope, based on requirements information gathered in various forms (requirements models and textual descriptions) during the requirements workshop. The requirements from different sources are compiled into one list and reconciled, since they are prepared by a number of participants in the requirements workshop using different notations (UML diagrams, SysML models, Story map) and may have differences in the vocabulary. Reconciliation encompasses the unification of vocabulary, as well as the identification and the removal of overlaps in requirements. The final list of requirements is used to estimate the scope of the project in story points. The estimate can be presented to the client and used as a basis for the project contract. A case study on the application of the proposed methodology is presented, using the animal shelter information system as a development project. It demonstrates that the methodology is viable and can facilitate the gathering of a more extensive set of requirements, thus ensuring a more detailed scope estimation.

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“…Linear Regression (LR) has long been used in software effort estimation practices, but in some cases, these models have limitations in accurately estimating the effort required (Sharma & Chaudhary, 2020). Several studies have been conducted to calculate effort in software development with algorithmic approaches such as Use Case Point, Function Point, and COCOMO (Feizpour et al, 2023;Kaushik et al, 2016;Park et al, 2016;Silhavy et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-based Linear Regression for Software Effort Estimation

Puguh Jayadi,

Khairul Adilah binti Ahmad,

Rayhan Zulfitri Dwi Cahyo

et al. 2024

Journal of Information System, Technology and Engineering

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In the context of software effort estimation, this study investigates the use of Particle Swarm Optimization (PSO)-based Linear Regression to improve estimation accuracy. The main problem faced is the limitations of standard Linear Regression models in accurately estimating the effort required for software development projects. This research aims to improve the quality of estimation of software efforts to optimize resource management and project schedules. The method used was the integration of PSOs in Linear Regression, which was evaluated using three different COCOMO datasets. Experimental results show that LR+PSO models consistently outperform standard Linear Regression with lower MAE, MSE, and RMSE, as well as higher R-squared. In conclusion, integrating PSOs in Linear Regression effectively improves the estimation accuracy of software efforts, demonstrating great potential for improving estimation quality in software project management practices.

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More Accurate Cost Estimation for Internet of Things Projects by Adaptation of Use Case Points Methodology

Cited by 3 publications

References 63 publications

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Compiling Requirements from Models for Early Phase Scope Estimation in Agile Software Development Projects

Particle Swarm Optimization-based Linear Regression for Software Effort Estimation

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