Calibrating use case points using bayesian analysis

Kan, Qi; Hira, Anandi; Venson, Elaine; Boehm, Barry

doi:10.1145/3239235.3239236

Cited by 7 publications

(9 citation statements)

References 11 publications

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“…The inflexibility of the use case complexity weight level impacts the accuracy of the estimation [10]. Moreover, the original complexity and assigned weight levels might not reflect the actual situations [45]. Fortunately, this was confirmed earlier by Karner [5] that the proposed complexity weight is based on the people's approximation at Objective Systems.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Ardiansyah

Ferdiana

Permanasari

2022

Int. J. Adv. Intell. Informatics

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Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant.

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Section: Related Workmentioning

confidence: 99%

“…The study of UCP calibration is conducted by Nassif et al [8] and followed by Qi et al [45]. Nassif et al [8] introduced a six-level use case complexity weight instead of a three-level as initially proposed by the original UCP.…”

Section: Related Workmentioning

confidence: 99%

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Ardiansyah

Ferdiana

Permanasari

2022

Int. J. Adv. Intell. Informatics

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“…A FUSP is an extended version of a USP that uses the Fuzzy Set theory to reduce some use case classification problems. Qi et al [35] improved the estimation accuracy of the UCP by using Bayesian analysis to calibrate the case complexity weights. Rak et al [36] proposed a model for effort estimation called Use Case Reusability (UCR).…”

Section: Related Workmentioning

confidence: 99%

Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

et al. 2022

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“…For instance, the three early‐phase transaction‐based effort estimation models proposed in our previous research 5 can be calibrated using the transactions identified from source code with tailored numbers of complexity attributes that are used to define early‐phase estimation models. Providing early‐effort estimates using early‐phase representations of transactions. The transactional information derived using the transaction analysis method proposed in this study is mapped into an abstract transaction model, which has many early phase representations 4,8‐10 . For example, a transaction can be modeled as user‐system interactions identified from use case narratives, as sequences of operations identified from object analysis diagrams, or as sequences of messages identified from sequence diagrams.…”

Section: Post‐development Calibration For Early Effort Estimationmentioning

confidence: 99%

“…The transactional information derived using the transaction analysis method proposed in this study is mapped into an abstract transaction model, which has many early phase representations. 4,[8][9][10] For example, a transaction can be modeled as user-system interactions identified from use case narratives, as sequences of operations identified from object analysis diagrams, or as sequences of messages identified from sequence diagrams. Early effort estimates can be made using those early-phase representations as input of the transaction-based effort estimation models calibrated using source code information.…”

Section: Post-development Calibration For Early Effort Estimationmentioning

confidence: 99%

Effort estimation of open source Android projects via transaction analysis

Kan

Boehm

2020

J Software Evolu Process

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Transactions have been used in many software sizing methods to measure software functional size and provide a basis for effort estimation. To further investigate the effects that transactions have on software functional size and more accurately estimate project effort using the transactional information, in this paper, we propose a method to identify transactions, as well as their complexity attributes that are influential to software functional size, directly from the source code of Android projects using static and dynamic analyses. The identified transactions are classified into different complexity levels and applied with different weights based on their evaluated complexity levels to distinguish their effects on software functional size. A size metric called sum of weighted transactions (SWT) is proposed to measure software functional size. We calibrate an effort estimation model using SWT based on a data set of 34 open source Android projects, and show the effectiveness of using SWT as a software functional size measure to estimate project effort.

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Calibrating use case points using bayesian analysis

Cited by 7 publications

References 11 publications

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

Effort estimation of open source Android projects via transaction analysis

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