][]  
Article  
Improvement of the Planning Process in Software  
Development Using the Use Case Point Method  
Mejoramiento del proceso de planificación en el desarrollo  
de software mediante el método de puntos de caso de uso  
Jose David Polo-Vanegas 1 , Yeinis Paola Espitia-Priolo 1  
and Daniel Jose Salas-Álvarez 1∗  
1
Department of Systems Engineering, Faculty of Engineering, University of Córdoba, Monteria, 230002, Colombia;  
jpolovanegas90@correo.unicordoba.edu.co; yespitiapriolo@correo.unicordoba.edu.co; danielsalas@correo.unicordoba.edu.co  
∗
Correspondence: danielsalas@correo.unicordoba.edu.co  
Citation: Polo, J.; Espitia, Y.; Salas, D. Improvement of the Planning Process in Software Development Using the Use Case Point  
Method. OnBoard Knowledge Journal 2025, 1, 5. https://doi.org/10.70554/OBJK2025.v01n01.06  
Received: 17/02/2025, Accepted: 29/04/2025, Published: 21/05/2025  
DOI: https://doi.org/10.70554/OBJK2025.v01n01.06  
Abstract: Planning is crucial for the success of software projects; however, it often faces problems such as lack of time,  
experience, and understanding of requirements. These challenges can lead to inaccurate estimates, delays, and products  
that do not meet customer expectations. This study aims to analyze the implementation of the Use Case Points (UCP)  
method to support planning in software development and make it more efficient. The research was carried out in several  
phases: first, common problems in software planning were analyzed; then, based on these, the UCP method was chosen  
and implemented in a solution supported by tools like Microsoft Excel. Finally, three case studies were conducted to  
evaluate the effectiveness of the UCP method and compare it with agile methodology, which allowed improvements in  
planning processes to be observed, in terms of time, in software project development.  
Keywords: Agile Methodology; Estimation; Software development planning; Software projects; Use Case Point Method  
(UCPM).  
Resumen: La planificación es crucial para el éxito de proyectos software; sin embargo, suele enfrentar problemas como  
la falta de tiempo, experiencia y comprensión de los requisitos. Estos desafíos pueden provocar estimaciones inexactas,  
retrasos y productos que no cumplen con las expectativas del cliente. Este estudio tiene como propósito analizar la  
implementación del método de puntos de casos de uso (MPCU) para apoyar la planificación en el desarrollo de software  
y hacerla más eficiente. La investigación se desarrolló en varias fases: primero, se analizaron los problemas comunes  
en la planificación de software; luego, con base en estos, se eligió el MPCU implementándolo en una solución apoyada  
en herramientas como Microsoft Excel, finalmente, se implementaron tres casos de estudio para evaluar la efectividad  
del MPCU, y compararlo con la metodología ágil, lo que permitió observar mejoras en los procesos de planificación en  
términos de tiempo, en el desarrollo de proyectos software.  
Palabras clave: Estimación; Método de puntos de casos de uso (MPCU); Metodología Ágil; Planificación del desarrollo  
de software; Proyectos de software.  
OnBoard Knowledge Journal 2025, 1, 5.  
https://revistasescuelanaval.com/obk/  
© 2026 by authors.  
Licensed by Escuela Naval de Cadetes "Almirante Padilla", COL.  
This article is freely accessible and distributed under the terms and conditions  
of Creative Commons Attribution (https://creativecommons.org/licenses/by/4.0/).  
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1. Introduction  
The software development process is essential for technological advancement and is closely linked  
to software engineering, which encompasses the structures, tools, and methods employed in the creation  
of computer programs. This process includes situation analysis, project drafting, software development,  
and the necessary testing to ensure proper functionality before implementation. In this context, planning  
plays a crucial role, as the success of the developed software depends on it. According to Alboka Soft, the  
benefits of good planning include realistic monitoring of each phase, reduction of unnecessary work and  
costs, evaluation of the impact of changes, stabilization of requirements, and absolute project traceability.  
Proper planning also grants independence to developers and ensures that the final objective adds maximum  
value to the project by organizing and documenting each step [1].  
Being competitive is a characteristic that companies require today, and therefore they must exercise  
greater control, making this goal increasingly difficult to achieve. Clients demand precise estimates and  
budgets related to software development projects in very short timeframes, indicating that incorrect responses  
and estimates lead to project failure and financial losses for companies. Consequently, preliminary project  
planning and clarification of objectives are necessary tasks for commercial success and the reasonable  
achievement of established goals. Neglecting this increases the potential for error and raises the level of  
failure and conflict [12].  
For a project to achieve its goal, it must consider all possible scenarios by consulting client needs.  
Failing to cover these possibilities can lead to project failure. Therefore, by improving planning capacity,  
optimal results may be achieved, reflected not only in company profits but also in customer satisfaction. This  
highlights the need for organizations to establish robust pre-production plans for projects.  
The annual report *The State of Software Development 2021*, based on a survey of over 500 software  
developers, identified the lack of effective planning as the primary cause of delays in software teams. The  
report emphasizes factors such as lack of time, insufficient experience, and inadequate understanding of  
requirements as major contributors to planning deficiencies [2]. Insufficient time often leads teams to rush or  
skip planning phases, resulting in poor requirement comprehension and increased errors during development.  
Meanwhile, lack of experience may hinder teams’ ability to properly structure planning activities, and limited  
understanding of project requirements can cause inaccurate estimations and overlooked complexities.  
Given these recurring problems, it is crucial for software development teams to dedicate sufficient time  
and effort to pre-production to ensure project success. Adequate planning supports the identification and  
mitigation of potential risks before they escalate, ultimately saving time and resources.  
This study aims to analyze the use of Use Case Point methods and the agile approach to verify their  
efficiency and understand the strengths and weaknesses of both methodologies.  
The structure of this article is organized as follows: Section 1 provides an overview of the importance  
of effective planning in software development and identifies common challenges. Section 2 details the  
main contributions of this study. Section 3 reviews related work and existing methodologies for project  
estimation and management. Section 4 describes the applied methodology. Section 5 presents the results and  
comparative analysis from the conducted case studies. Finally, Section 7 summarizes the conclusions and  
discusses future directions.  
2. Contributions  
This section summarizes the key contributions of the study aimed at enhancing the software devel-  
opment planning process. By addressing common challenges in estimation and project management, the  
research proposes an integrated methodology combining the Use Case Point Method with agile practices.  
The following points highlight the main advancements and practical outcomes achieved through this work.  
   
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i.  
This study identifies and explores the primary challenges affecting software development planning,  
such as lack of time, insufficient experience, and limited understanding of requirements, factors that  
often lead to inaccurate estimates and project delays.  
ii.  
iii.  
iv.  
A solution based on the UCP methodology supported by tools like Microsoft Excel was developed  
to facilitate effort estimation in man-hours for software projects. This implementation incorporates  
technical and environmental factors to enhance planning accuracy.  
A comparative analysis between UCP and the Agile Scrum methodology was conducted, highlighting  
the strengths and weaknesses of each approach. Furthermore, a hybrid approach is proposed to  
leverage UCP’s initial estimation accuracy alongside Agile’s flexibility during project execution.  
The proposed solution was evaluated using three case studies (one fictitious and two real), demon-  
strating improvements in effort estimation and time management in software development projects,  
thereby evidencing the effectiveness and applicability of the hybrid approach in different contexts.  
3. Related Works  
The planning process in software development is a relevant activity because good project planning is  
crucial for its success, contributing to effective team management and significantly improving product quality  
]. To improve the software planning process, it is first necessary to understand what is being done and how  
it is being done, since many software projects fail due to poor requirements management and unrealistic  
deadlines [ ]. Another cause is the lack of rigorous and detailed estimation, which allows clarity regarding  
[5  
4
the activities to be performed both in pre- and post-production, as well as their possible changes, leaving a  
sufficiently clear margin for potential errors. This often results in uncertainty during development about  
what to do in certain situations or how to handle changing requirements [11].  
Based on the above, a question arises: what methods do developers use to mitigate these problems?  
A common technique used by developers for software estimation is the use of work breakdown structures  
(WBS), which involve dividing the project into smaller, manageable tasks, facilitating the estimation of the  
time and resources needed to complete each task. Another widely used technique is function points, which  
measure the functional size of the software that is, how many functions the software performs and how  
complex they are. From this measurement, the effort required to develop the software can be estimated.  
Following this path, there is no single method that guarantees success in all cases, but many researchers  
agree that the agile model is the best for software development projects due to its flexibility in responding to  
changes and new requirements [10].  
The Agile method is so popular that Ortiz Álvarez presented a tool for managing activities in software  
projects using an agile methodology based on Scrum, which involves weekly follow-up meetings and delivery  
cycles of activities. The tool allows the creation of a client space where tasks can be recorded and edited to  
ensure direct communication with the development team [16].  
However, the agile approach has several disadvantages, including the lack of a detailed plan, which  
can make it difficult to estimate the time and resources needed to complete the project. This is a risk  
when considering the function point technique, where if not applied correctly, resulting estimates may be  
inaccurate or incomplete. For example, if all the software functions are not identified or their complexity is  
underestimated, the estimate may be too low, leading to problems such as delivery delays or cost overruns.  
Therefore, it is important to correctly apply this technique and ensure that all relevant functions are properly  
identified and evaluated [11].  
In a review of software project case studies, Ibraigheeth Mohammad and Fadzli Syed Abdullah identified  
common factors contributing to software project success: successful software projects have realistic and stable  
objectives, a team with adequate knowledge and experience, efficient technology, user involvement, and  
efficient management. Additionally, they note that project failures can be useful for identifying key factors  
for project success. They assert that no single factor guarantees project success; rather, it is a combination of  
several factors that contribute to success. Understanding these key success factors can help project managers  
make informed decisions about resource allocation and project management [6].  
 
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Project success is measured by the ability to complete it according to desired specifications, within the  
specified budget and promised schedule, while keeping the client and stakeholders satisfied. For correct  
project completion, both planning and execution must be properly implemented.  
Furthermore, software defect prediction is one of the most active research areas in software engineering  
and plays an important role in software quality assurance. The increasing complexity and reliance on  
software have raised the difficulty of delivering high-quality, low-cost, and maintainable software, as well as  
the likelihood of creating software defects. Software defects often cause incorrect or unexpected results and  
behaviors in undesirable ways. Defect prediction is a crucial and essential activity. Using defect predictors  
can reduce costs and improve software quality by identifying modules (instances) prone to defects before  
testing, enabling software engineers to effectively optimize the allocation of limited resources for testing and  
maintenance [7].  
In addition to recent advances in software defect prediction, ensemble learning approaches have been  
explored. These approaches combine multiple classification techniques to improve prediction performance.  
This research provides a systematic review of the use of ensemble learning for software defect prediction,  
identifying the most employed methods and their performance metrics. Results show that ensemble ap-  
proaches, such as random forests and boosting, offer better classification accuracy compared to individual  
classifiers. Furthermore, the importance of feature selection and data sampling as preprocessing steps to  
improve ensemble classifiers is highlighted [8].  
4. Methodology  
This study focused on conducting a comprehensive and detailed analysis of case studies and reports  
collected from software development companies that have employed the Use Case Point (UCP) method.  
The UCP method, which serves as a systematic approach to estimating the effort and resources required  
in software projects, was critically examined to gain a deeper understanding of its practical application.  
Particular attention was given to identifying common challenges, potential sources of error, and limitations  
that may arise during its implementation, such as estimator subjectivity and variability in assessing use case  
complexity and environmental factors.  
In parallel, the study performed a comparative evaluation between the UCP method and the agile  
approach, a widely adopted and flexible methodology known for its iterative planning and adaptability to  
change throughout the software development lifecycle. This comparative analysis explored the strengths and  
weaknesses of both methods in the context of project planning accuracy, flexibility, effort estimation, and risk  
management. By analyzing these aspects, the research sought to provide a more nuanced and comprehen-  
sive perspective on how software development planning processes could be optimized by leveraging the  
complementary benefits of both approaches.  
Based on the insights gained from this analysis, a novel solution was proposed and developed that  
integrates the Use Case Point method within an agile framework, aiming to address the identified problems in  
traditional software planning practices. This hybrid approach was designed to offer a solid initial estimation  
grounded in UCP’s structured assessment while maintaining the iterative flexibility and responsiveness  
characteristic of agile methodologies.  
To evaluate the practicality and effectiveness of this integrated solution, three case studies were con-  
ducted: one fictitious project designed to test the approach in a controlled scenario, and two real-world  
projects undertaken by students in a systems engineering program. These case studies provided empirical  
data to assess the solution’s applicability across different contexts, allowing for comparison of estimated  
effort, time management, and overall planning improvements. The outcomes from these studies served as a  
foundation for validating the proposed methodology and identifying areas for further refinement.  
5. Results  
5.1. Comparative analysis of MPCU and Agile method  
The Use Case Points estimation method proposed by Karner is used as a basis to calculate the effort  
required for software implementation. This method allows an early estimation based on a certain knowledge  
   
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of the requirements to be developed, which is of interest to companies engaged in software construction.  
The effort estimation, measured in man-hours (MH), required for the development of a specific software  
product is performed based on the number and complexity of use cases identified in the project. However,  
the effort estimation for the implementation process of information systems using this method shows a  
significant deviation from the actual estimated effort. One influencing factor in this deviation may be the lack  
of experience and subjectivity of the estimator when assigning values to technical and environmental factors,  
as well as when classifying the complexity levels of use cases and actors [15].  
The agile methodology Scrum, one of the most widely used, is characterized by its flexibility and  
adaptability to changes and new requirements that may arise during the development process. Instead  
of following a rigid plan, the agile model focuses on the continuous delivery of small parts of the project,  
called iterations or sprints, which improve over time. However, agile has several disadvantages, such as the  
difficulty of estimating the time and resources necessary to complete the project due to the lack of a detailed  
plan (Table 1).  
Table 1. Comparative table of the MPCU and the Agile Method (Scrum).  
Aspect  
Use Case Points (UCP)  
Agile Methodology  
Based on the number and complexity Estimated based on iterations  
Estimation Basis  
of identified use cases.  
(sprints) and smaller scope tasks.  
Lower initial accuracy due to the it-  
erative and flexible nature of the pro-  
cess.  
High accuracy in initial estimation if  
requirements are well-defined.  
Initial Accuracy  
Effort is calculated in man-hours, Effort is continuously adjusted each  
considering technical and functional sprint, without a detailed global esti-  
Effort Calculation  
complexity.  
mate at the start.  
Changes are easily incorporated in  
the next sprint without greatly affect-  
ing the overall estimate.  
Requires major revisions and adjust-  
ments if requirements change.  
Adaptation to Changes  
Estimation Deviation  
Prerequisites  
Higher risk of deviation if use cases Lower risk of deviation, as estimation  
are not correctly identified or com- is continuously adjusted based on ac-  
plexity is underestimated.  
tual project progress.  
Requires clear and detailed knowl- Does not require complete knowl-  
edge of requirements from the begin- edge of requirements, as they may  
ning.  
change during the project.  
Low flexibility, since estimation is High flexibility, as estimation adapts  
done at the start and depends on re- in each sprint according to new needs  
Flexibility in Estimation  
Impact of Complexity  
quirement stability.  
or changes.  
Use case complexity directly affects Complexity is managed iteratively,  
the estimate, potentially causing de- adjusting the estimate each sprint  
viations if not measured properly.  
based on the team’s capabilities.  
Source: The authors.  
5.2. Implementation of the MPCU using the tool  
Based on the research, a solution was developed to apply the Use Case Point Method (UCP) within  
the Scrum-based methodology: we took the UCP implementation from an Excel spreadsheet presented by  
Scott Sehlhorst in a Tyner Blain article [14], translated it into Spanish, and applied recommendation tables to  
facilitate its use along with a simple spreadsheet to organize scrums, enabling its combination with the agile  
methodology (Figure 1).  
The spreadsheet originally contained five tabs for processing and collecting the data necessary to  
estimate effort using the Use Case Point Method (UCP). Later, we added a tab called "Scrum" to facilitate  
comparison with the agile methodology.  
To calculate Use Case Points (UCP), the first step is to determine a numerical representation of the  
technical factors of the software, known as the Technical Complexity Factor (TCF), which covers non-  
 
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Figure 1. Spreadsheet tabs.  
Source: The authors.  
functional aspects of the system such as performance, security, and the use of reusable components. The  
second step is to create a number representing the environmental factors that influence the team’s ability to  
perform the work, called the Environmental Factor (EF), which includes characteristics of the implementation  
team and the process, such as team experience and requirements stability.  
The third step is to measure the use cases and create a representation of the quantity and complexity of  
the use cases that the software must support, called Unadjusted Use Case Points (UUCP), classifying use  
cases as simple, average, or complex.  
In the fourth step, the software users—both people and other systems—are analyzed, and actors are  
classified as simple, average, or complex (Actor Weight, AW).  
Finally, the formula to calculate Use Case Points is described in terms of variables such as TCF, EF,  
UUCP, and AW [13].  
In the UCP cell (see Figure 2), equation 1 is used to calculate the Use Case Points. These points are then  
multiplied by the “Ratio,” which represents the effort hours per use case point, to obtain the effort hours for  
the project’s use cases. This translates into the estimated programming hours for those use cases.  
For the total project calculation, we recommend that programming time be 40% of the total development  
time, based on the work of Suresh Nageswaran [9]. For the remaining development activities, the percentage  
of total time allocated to each can be chosen freely. As shown in the spreadsheet (see Figure 2), these  
recommendations are based on the work of Lianny O’Farrill Fernández [3].  
UCP = (UUCP + AW) × TCF × EF  
(1)  
Figures 2 to 7 show the spreadsheets corresponding to each factor.  
With the changes and additions made in this implementation of the Use Case Point Method (UCP), it  
was possible to compare the initial estimations obtained by both methodologies, with the UCP providing  
estimates that were more distant than those made by developers using Scrum.  
5.3. Implemented case studies  
Three tests of the solution were conducted as follows:  
Case 1: Geek Web – Communication and socialization platform for communities (not executed).  
Geek Web is a communication platform designed to connect people with shared interests, allowing them to  
share knowledge, experiences, and hobbies. The idea arises from the need to create a space where enthusiasts  
of specific topics can find and interact with others who share their interests. Its objectives are to create a  
secure and accessible communication platform for interest-based communities and to facilitate connection  
and knowledge exchange among like-minded individuals (Table 2).  
For the case studies, students of Systems Engineering at the University of Córdoba, who were working on  
their respective projects, were asked to complete the technical complexity, environmental factors, actors, and  
use cases for the estimation of Use Case Points (UCP) and consequently fill in the "Scrum" tab, emphasizing  
the estimates they considered.  
Case 2: Medical Appointment Scheduling at Camus (Testing Phase). At Camus, the medical appoint-  
ment scheduling system combines in-person service by quota and phone calls, resulting in long queues of  
users waiting to be attended. The manual scheduling process can take up to 25 minutes. Since many of the  
people who come to schedule appointments are from remote villages or distant places, quotas are limited,  
and therefore, not everyone is attended to on the same day. This causes people to have to queue again to  
reserve a spot. The objectives are to implement an efficient and accessible medical appointment scheduling  
   
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Figure 2. Final calculations spreadsheet.  
Source: The authors.  
Table 2. MPCU results for Geek Web.  
Factor  
Weight  
TCF  
1.195  
0.815  
105  
EF  
UUCP  
AW  
8
UCP  
110.1  
28  
Ratio  
Effort Hours  
3081  
Source: The authors.  
system, reduce waiting times and queues, increase the user service capacity, and improve user experience  
(Table 3).  
Case 3: University Wheels (Testing Phase). In the city of Montería, located in the department of  
Córdoba, mobility is a fundamental aspect for the development of its inhabitants, particularly for university  
students. The university transportation system plays a crucial role in seeking an efficient and reliable solution  
to facilitate mobility within the city and ensure that students can access their educational institutions in a  
timely and safe manner. Its objectives are to improve the safety of students and the community, offer an  
efficient and accessible transportation service, and facilitate the movement of students to their educational  
institutions and the community to their destinations of interest (Table 4).  
Table 5 presents a comparison of effort estimations, measured in hours, obtained through the Use Case  
Point Method (UCP) and the Scrum methodology for three different projects. This comparison highlights  
   
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Figure 3. Technical factors spreadsheet.  
Source: The authors.  
Table 3. MPCU results for Scheduling Medical Appointments at the Campuses.  
Factor  
Weight  
TCF  
1.015  
0.74  
25  
EF  
UUCP  
AW  
2
UCP  
20.3  
28  
Ratio  
Effort Hours  
568  
Source: The authors.  
differences in estimation approaches and provides insight into how each method evaluates the time required  
for software development. Table 5 presents a comparison of effort estimations, measured in hours, obtained  
through the Use Case Point Method (UCP) and the Scrum methodology for three different projects. This  
comparison highlights differences in estimation approaches and provides insight into how each method  
evaluates the time required for software development.  
 
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Figure 4. Environmental factors spreadsheet.  
Source: The authors.  
Figure 5. Use case spreadsheet.  
Source: The authors.  
6. Discussion  
Authors should discuss the results and how they can be interpreted in the context of previous studies  
and working hypotheses. The results and their implications should be discussed in the broadest possible  
context.  
7. Conclusions  
The primary focus of this study was to conduct an appreciative observation of the current state of  
software planning by using the agile methodology as a benchmark and thoroughly exploring the strengths  
and limitations of the Use Case Point (UCP) Method. To facilitate the implementation and evaluation of the  
proposed hybrid solution, the widely accessible accounting tool Microsoft Excel was employed, enabling a  
practical and replicable estimation process. The case studies revealed a notable divergence between the effort  
 
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Figure 6. Actor spreadsheet.  
Source: The authors.  
Figure 7. Scrum organization spreadsheet.  
Source: The authors.  
Table 4. MPCU results for University Rounds.  
Factor  
Weight  
TCF  
1.09  
0.77  
50  
EF  
UUCP  
AW  
6
UCP  
47  
Ratio  
Effort Hours  
20  
940  
Source: The authors.  
estimates generated by the UCP method and the initially more “optimistic” projections derived from the  
Scrum methodology, highlighting differences in estimation philosophies and potential biases.  
These findings underscore the value of integrating the structured and systematic nature of UCP with the  
adaptive and iterative characteristics of agile methods. It is anticipated that this combination can provide a  
robust and reliable initial estimate while simultaneously maintaining the flexibility necessary to accommodate  
evolving requirements and unforeseen changes during the software development lifecycle. Following the  
establishment of a solid baseline estimate, project teams are positioned to leverage agile principles for ongoing  
project execution and refinement.  
Nevertheless, the study emphasizes the importance of managing requirement changes carefully, as  
excessive modifications during development can undermine the accuracy of the preliminary estimate and  
necessitate continuous re-planning and adjustment for both methodologies. Future research is recommended  
   
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Table 5. MPCU vs SCRUM results.  
Project  
MPCU (Hours) Scrum (Hours) Remarks  
The estimated time is lower in Scrum  
than in UCP. Here, only programming  
2266 time is estimated, not the entire project  
(analysis, design, programming, test-  
ing, etc.).  
Geek Web  
3081.4987  
Medical Appointment Scheduling  
University Wheels  
567.8316  
140  
Total project time is estimated. UCP es-  
1230 timation remains less "optimistic" than  
Scrum.  
1504.0256  
Source: The authors.  
to evaluate the scalability and adaptability of this integrated approach in more complex and larger-scale  
software development environments, thereby further validating its applicability and identifying opportunities  
for enhancement.  
Author Contributions: Jose Polo: Conceptualization, Methodology, Software, Visualization, Validation, Formal analysis.  
Yeinis Espitia: Investigation, Resources, Data curation, Writing – original draft. Daniel Salas: Writing – review & editing,  
Supervision, Project administration, Funding acquisition.  
All authors have read and agreed to the published version of the manuscript. Please refer to the CRediT taxonomy for the  
definitions of the terms. Authorship is limited to those who have made substantial contributions to the reported work.  
Funding: This research received no external funding.  
Institutional Review Board Statement: Not applicable, since the present study does not involvehuman personnel or  
animals.  
Informed Consent Statement: This study is limited to the use of technological resources, so nohuman personnel or  
animals are involved.  
Data Availability Statement: As previously stated, the implemented algorithms, the generated data as well as the respec-  
tive analysis will be available in a public repository that can be accessed at https://github.com/Jcuetomorelo37/ryu_monitor  
Acknowledgments: Thanks to the University of Córdoba for providing technical and logistical support during the  
distinct phases of this research, and special thanks to for his constant commitment and supervision since the beginning of  
the cycle of this research.  
Conflicts of Interest: Under the authorship of this research, it is declared that there is no conflict of interest with the  
present research.  
References  
1. Alboka Soft (2023). Planificar en un proyecto de software. https://www.albokasoft.com/index.php/blog/80  
-proyecto-de-software-a-medida. Accessed: Apr. 24, 2023.  
2. Coding Sans (2021). The state of software development 2021.  
3. Fernández, L. O. (2010). Estimación de tiempo y esfuerzo en proyectos de software. Technical report, Universidad  
Central Marta Abreu de las Villas.  
4. Garzón, E. (2019). Elaboración de un modelo para la implementación de controles eficaces para la gestión de riesgos  
en proyectos de software bajo el marco de estándares internacionales en empresas fábricas de software en bogotá.  
Technical report, Universidad Militar Nueva Granada.  
5. Ibeto, O. E., Gbadegesin, M., Fakunle, I., and Wunmi, A. S. (2022). Software project planning, people management,  
and effects on product quality. American Journal of Computer Science and Information Technology, pages 2–9.  
6. Ibraigheeth, M. and Fadzli, S. A. (2019). Core factors for software projects success.  
7. Li, Z., Jing, X. Y., and Zhu, X. (2018). Progress on approaches to software defect prediction.  
8. Matloob, F. et al. (2021). Software defect prediction using ensemble learning: A systematic literature review.  
9. Nageswaran, S. (2001). Test effort estimation using use case points.  
10. Nakigudde, S. (2019). Project management models and software development project success. ResearchGate.  
                     
OnBoard Knowledge Journal 2025, 1, 5  
12 of 12  
11. O’Regan, G. (2017). Software Project Management. SpringerLink.  
12. Pérez, A. (2021). Errores a evitar en la planificación de recursos de un proyecto. https://www.obsbusiness.school/  
blog/errores-evitar-en-la-planificacion-de-recursos-de-un-proyecto. Accessed: Apr. 24, 2023.  
13. Sehlhorst, S. (2007a). Software cost estimation with use case points – final calculations. Tyner Blain. Feb. 19, 2007.  
14. Sehlhorst, S. (2007b). Software cost estimation with use case points – free excel spreadsheet. Tyner Blain. Feb. 20,  
2007.  
15. Vazquez, P., Panizzi, M., and Bertone, R. (2018). Estimación del esfuerzo del proceso de implantación de software  
basada en el método de puntos de caso de uso. Technical report, Universidad Tecnológica Nacional.  
16. Álvarez, B. O. (2022). Herramienta para la gestión de actividades en los proyectos de software. Technical report,  
Universidad de Antioquia.  
Authors’ Biography  
Jose David Polo-Vanegas Systems engineering student at the University of Córdoba.  
Yeinis Paola Espitia-Priolo Systems engineering student at the University of Córdoba.  
Daniel Jose Salas-Álvarez Master’s Degree in Computer Science from the Industrial  
University of Santander.  
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