Building New Tool for Software Project Planning Based on Meta-modeling Concepts and CMMI Meta-Standards

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method.

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Software Project Management Systems Using Kanban Method in the CV. Primavisi Globalindo

INTENSIF Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi ◽

10.29407/intensif.v4i2.14320 ◽

2020 ◽

Vol 4 (2) ◽

pp. 215-231

Author(s):

Muhammad Aliyya Ilmi ◽

Fajar Pradana ◽

Widhy Hayuhardhika Nugraha Putra

Keyword(s):

Project Management ◽

Project Planning ◽

Software Project ◽

Unit Testing ◽

Functional Requirements ◽

Software Projects ◽

Management Planning ◽

Scheduling System ◽

Usability Test ◽

Project Management And Scheduling

Reducing the risk of failure in working on software projects is one of the successes for the company. It can be done by implementing project planning management properly. One important aspect of project management planning is scheduling. Scheduling includes recording human resources and tasks in the project. The Kanban method is one of the methods used to overcome problems in controlling project schedules. This study aims to develop a project scheduling system that applies the Kanban method. In this research, project management and scheduling system will be developed using the Kanban method. This system expected to be able to assist companies in handling projects. Kanban was chosen because it can easily respond to project changes, easily implemented, and company needs. In this study, unit testing was performed on the system's three main features and tested the validity of the system's 49 functional requirements. The usability test produces a value of 76. Based on the validation and usability test results, it can be concluded that the system is included in the acceptable category.

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Fuzzy Rules for Risk Assessment and Contingency Estimation within COCOMO Software Project Planning Model

Exploring Innovative and Successful Applications of Soft Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4785-5.ch006 ◽

2014 ◽

pp. 88-111 ◽

Cited By ~ 1

Author(s):

Ekananta Manalif ◽

Luiz Fernando Capretz ◽

Danny Ho

Keyword(s):

Risk Assessment ◽

Risk Management ◽

Project Planning ◽

Project Managers ◽

Research Data ◽

Software Project ◽

Planning Model ◽

Effort Estimation ◽

New Approach ◽

Project Risks

Software development can be considered to be the most uncertain project when compared to other projects due to uncertainty in the customer requirements, the complexity of the process, and the intangible nature of the product. In order to increase the chance of success in managing a software project, the project manager(s) must invest more time and effort in the project planning phase, which involves such primary and integrated activities as effort estimation and risk management, because the accuracy of the effort estimation is highly dependent on the size and number of project risks in a particular software project. However, as is common practice, these two activities are often disconnected from each other and project managers have come to consider such steps to be unreliable due to their lack of accuracy. This chapter introduces the Fuzzy-ExCOM Model, which is used for software project planning and is based on fuzzy technique. It has the capability to not only integrate the effort estimation and risk assessment activities but also to provide information about the estimated effort, the project risks, and the effort contingency allowance necessary to accommodate the identified risk. A validation of this model using the project’s research data shows that this new approach is capable of improving the existing COCOMO estimation performance.

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Fuzzy Rules for Risk Assessment and Contingency Estimation Within COCOMO Software Project Planning Model

Global Business Expansion ◽

10.4018/978-1-5225-5481-3.ch035 ◽

2018 ◽

pp. 771-797

Author(s):

Ekananta Manalif ◽

Luiz Fernando Capretz ◽

Danny Ho

Keyword(s):

Risk Assessment ◽

Risk Management ◽

Project Planning ◽

Project Managers ◽

Research Data ◽

Software Project ◽

Planning Model ◽

Effort Estimation ◽

New Approach ◽

Project Risks

Software development can be considered to be the most uncertain project when compared to other projects due to uncertainty in the customer requirements, the complexity of the process, and the intangible nature of the product. In order to increase the chance of success in managing a software project, the project manager(s) must invest more time and effort in the project planning phase, which involves such primary and integrated activities as effort estimation and risk management, because the accuracy of the effort estimation is highly dependent on the size and number of project risks in a particular software project. However, as is common practice, these two activities are often disconnected from each other and project managers have come to consider such steps to be unreliable due to their lack of accuracy. This chapter introduces the Fuzzy-ExCOM Model, which is used for software project planning and is based on fuzzy technique. It has the capability to not only integrate the effort estimation and risk assessment activities but also to provide information about the estimated effort, the project risks, and the effort contingency allowance necessary to accommodate the identified risk. A validation of this model using the project's research data shows that this new approach is capable of improving the existing COCOMO estimation performance.

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