Implementation of use case point as software effort estimation in Scrum Framework

Yuliansyah, Herman; Qudsiah, Sarah Nurul; Zahrotun, Lisna; Arfiani, Ika

doi:10.1088/1757-899x/403/1/012085

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…UCP can also collaborate with the Scrum Framework by connecting between User Stories on the Product Backlog and Use Case on UCP as performed by Yuliansyah et al (2018). In their study, UCP adjusted User Stories on Product Backlog by adding the transactions attribute on a user story, and thereby transforming it to Functionality.…”

Section: Conducting Phase Resultsmentioning

confidence: 99%

“…(Abrahamsson et al, 2011), (Malgonde & Chari, 2018), (Zakrani et al, 2018) Table 9 Types of software effort estimation method and approach Complexity attribute is interpreted in different aspects, and most study understands it as the complexity of the project (Popli & Chauhan, 2014b;Garg & Gupta, 2015;Tanveer et al, 2016;Tanveer et al, 2017b;Bilgaiyan et al, 2018). Some research used it to represent technical complexity (Hamouda, 2014;Khatri et al, 2016;Yuliansyah et al, 2018). Also, environmental complexity is a part of the attribute that applies in two research (Hamouda, 2014;Khatri et al, 2016).…”

Section: Approach Methodsmentioning

confidence: 99%

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A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

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Currently, Agile software development method has been commonly used in software development projects, and the success rate is higher than waterfall projects. The effort estimation in Agile is still a challenge because most existing means are developed based on the conventional method. Therefore, this study aimed to ascertain the software effort estimation method that is applied in Agile, the implementation approach, and the attributes that affect effort estimation. The results showed the top three estimation that is applied in Agile, are machine learning (37%), Expert Judgement (26%), and Algorithmic (21%). The implementation of all machine learning methods used a hybrid approach, which is a combination of machine learning and expert judgement, or a mix of two or more machine learning. Meanwhile, the implementation of effort estimation through a hybrid approach was only used in 47% of relevant articles. In addition, effort estimation in Agile involved twenty-four attributes, where Complexity, Experience, Size, and Time are the most commonly used and implemented.

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Section: Conducting Phase Resultsmentioning

confidence: 99%

Section: Approach Methodsmentioning

confidence: 99%

A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

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“…This technique has less empirical evidence regarding accuracy and is less applicable in the software industry. This technique is not a predictive base [33][34][35][36].…”

Section: Planning Pokermentioning

confidence: 99%

A software-based cost estimation technique in scrum using a developer's expertise

Butt

Khalid²,

Ercan³

et al. 2022

Advances in Engineering Software

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“…Technology is represented by indicators of diversity and sophistication or novelty. Finally, coding is an indicator for developing a program, while the size and complexity indicators represent the database (Hamouda, 2014;Khatri et al, 2016;Pasuksmit et al, 2021;Rosa et al, 2021;Sommerville, 2011;Yuliansyah et al, 2018).…”

Section: Questionnairementioning

confidence: 99%

Significant Factors in Agile Software Development of Effort Estimation

Sudarmaningtyas¹,

Mohamed²

2022

JST

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The Agile effort estimation involves project-related and people-related factors. This research objective is to find the factors that influence Agile effort estimation significantly through path analysis using a structural equation model. This research built an agile effort estimation path coefficient model from six constructs from theories and previous studies. Project-related factors represent by requirement and design implementation constructs. People-related factors are measured by the construct of experience, knowledge, and technical ability. The last construct is the effort itself. SmartPLS is employed for the confirmatory composite analysis and the structural model assessment. The confirmatory composite analysis indicated that all constructs are reliable and valid. Furthermore, the structural model assessment found that all factors of project-related constructs have a positive relationship and significant influence, showing a coefficient path value of 59.1% between requirement and design implementation constructs. All constructs represent people-related factors indicated by the coefficient path value of 67% between experience and knowledge, 42.6% between experience and technical ability, and 54.4% between knowledge and technical ability. In addition, all constructs proved influential simultaneously to effort by 31.1%. Positively contribute provided by requirement, experience, and technology’s ability. Significantly influenced provided by constructs of the developer’s knowledge and technical ability. The largest effect is given by technical ability, knowledge, and experience on medium and small scales. Contrarily, both constructs from project-related effects can be negligible because there was no influence. Based on the result, this study concludes that the significant factors in Agile effort estimation are technical ability, knowledge, and experience.

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Implementation of use case point as software effort estimation in Scrum Framework

Cited by 11 publications

References 8 publications

A Review Article on Software Effort Estimation in Agile Methodology

A Review Article on Software Effort Estimation in Agile Methodology

A software-based cost estimation technique in scrum using a developer's expertise

Significant Factors in Agile Software Development of Effort Estimation

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