Use Case Points Method of Software Size Measurement Based on Fuzzy Inference

Xie, Yue; Guo, Jun; Shen, Anwei

doi:10.1007/978-3-319-11104-9_2

Cited by 5 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It tried to smoothen over the abrupt classification by providing a continuous and gradual classification. Early works in discretizing the current complexity weight level was proposed [7], [6], [10]. Wang et al [7] introduced the complexity weight level from three to five.…”

Section: Related Workmentioning

confidence: 99%

“…The result showed that EUCP was more effective than UCP for the two projects. Xie et al [6] proposed discretizing complexity weight level that extends the usecase complexity from three to four-level. The result showed that the proposed complexity weight increased by 5.5 (person-hours) with an error rate of 15.45% using four real project data sets.…”

Section: Related Workmentioning

confidence: 99%

“…The original UCP framework appointed complexity weight levels as 5 for Simple, 10 for average, and 15 for the complex. Researchers have criticized the origin complexity weight level as the complexity hierarchy level is discontinuous, which can sometimes result in inaccurate measurements [6] and abrupt classification of use case [7], [8]. For example, the use case of 8 transactions has double the weight of the use case with seven transactions.…”

Section: Introductionmentioning

confidence: 99%

“…Several works have been conducted to solve the abrupt classification problem of the use case complexity weight level. For instance, the existing fuzzy approaches are utilized and proposed [6], [7], [9], [10]. Fuzzy logic is always used to discretize the existing complexity weight level.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Ardiansyah

Ferdiana

Permanasari

2022

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Ardiansyah

Ferdiana

Permanasari

2022

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

show abstract

“…Fuzzy reasoning is a computation process that uses fuzzy logic methods to obtain new fuzzy propositions as conclusions under the condition of given fuzzy propositions [32]. Fuzzy reasoning can be divided into fuzzification, fuzzy logic reasoning, and defuzzification.…”

Section: B Fuzzy Regressionmentioning

confidence: 99%

Repetitive Control Process for Periodic Disturbance Cancellation Using Data Classification With a Fuzzy Regression Approach

Lin

2020

IEEE Access

View full text Add to dashboard Cite

The utilization of a repetitive controller to cancel periodic disturbance or noise in mechanical systems has become increasingly important for industrial applications. In this study, a repetitive control process was developed using a data classification method, with a fuzzy regression approach and basis function, to reduce tracking errors in feedback controllers. First, a system model using the basis function is illustrated to compute the matched basis functions and their associated coefficients. A real case example of improving the focus of an electron beam subjected to periodic fluctuations has been described for verification and error analysis. Next, model algorithms containing pure and fuzzy regression are introduced into a repetitive feedback control system to reduce tracking errors caused by a periodic disturbance. System output data are categorized using fuzzy inference rules as similar data forming a single group are typically more reliable than the entire output data. The fuzzy theorem approach adopts a Gaussian membership function for system output variables owing to uncertainties that arise from modeling errors, environmental noise, etc. It is determined that the repetitive control process based on data classification with a fuzzy regression approach is more effective than using a pure regression approach. Increasing the number of data classifications initially improves accuracy; however, this decreases when the number of data classifications continues to increase. The optimal root-mean-square output tracking error convergence value was determined as 10 -14.09 when the system output data was classified into four categories, demonstrating the satisfactory reduction of a periodic disturbance. Similar results were obtained using the pure regression algorithm, where the lowest averaged verification error was 2.71% for the linear basis function model, with data classified into four categories, and this corresponded to an average prediction error of 3.86%.

show abstract