Categorical Variable Segmentation Model for Software Development Effort Estimation

Silhavy, Petr; Šilhavý, Radek; Prokopová, Zdenka

doi:10.1109/access.2019.2891878

Cited by 54 publications

(29 citation statements)

References 30 publications

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“…The main goal is to evaluate whether models based on IFPUG estimate effort more accurately than the original IFPUG approach. In this study, a following methods [24] are used:…”

Section: Methods Used In Studymentioning

confidence: 99%

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Spectral Clustering Effect in Software Development Effort Estimation

2021

Self Cite

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Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.

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“…The main goal is to evaluate whether models based on IFPUG estimate effort more accurately than the original IFPUG approach. In this study, a following methods [24] are used:…”

Section: Methods Used In Studymentioning

confidence: 99%

“…In [24], a categorical variable segmentation (CVS) model was introduced. The CVS model is based on dataset segmentation, where the relative project size (see Table 1) is used as a segmentation parameter.…”

Section: Related Workmentioning

confidence: 99%

Spectral Clustering Effect in Software Development Effort Estimation

2021

Self Cite

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“…Table VII shows how nominal data were handled in the selected papers. Using regression, four techniques were identified: Transformation to dummy variables, dataset segmentation, interaction, and use of a hierarchical linear model [30], [31], [36], [41], [42]. Using CBR, the equality distance was used to assess the similarity between projects that are described by nominal features [1], [36].…”

Section: F Handling Of Categorical Data In Sdee (Mq6)mentioning

confidence: 99%

Handling of Categorical Data in Software Development Effort Estimation: A Systematic Mapping Study

Amazal

Idri²

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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Producing reliable and accurate estimates of software effort remains a difficult task in software project management, especially at the early stages of the software life cycle where the information available is more categorical than numerical. In this paper, we conducted a systematic mapping study of papers dealing with categorical data in software development effort estimation. In total, 27 papers were identified from 1997 to January 2019. The selected studies were analyzed and classified according to eight criteria: publication channels, year of publication, research approach, contribution type, SDEE technique, Technique used to handle categorical data, types of categorical data and datasets used. The results showed that most of the selected papers investigate the use of both nominal and ordinal data. Furthermore, Euclidean distance, fuzzy logic, and fuzzy clustering techniques were the most used techniques to handle categorical data using analogy. Using regression, most papers employed ANOVA and combination of categories.

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“…Decision making, process control, information management, and query processing are only a few of the applications for the newly discovered experience. As a result, data mining is recognized as one of the most exciting modern database technologies in the information industry, as well as one of the most important frontiers in database systems [1,2]. This chapter will go into the basics of data mining as well as the data extraction techniques.…”

Section: Introduction 11 Data Miningmentioning

confidence: 99%

The Concept of Data Mining

Ogunleye¹

2022

Artificial Intelligence

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Data mining is a technique for identifying patterns in large amounts of data and information. Databases, data centers, the internet, and other data storage formats; or data that is dynamically streaming into the network are examples of data sources. This paper provides an overview of the data mining process, as well as its benefits and drawbacks, as well as data mining methodologies and tasks. This study also discusses data mining techniques in terms of their features, benefits, drawbacks, and application areas.

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Categorical Variable Segmentation Model for Software Development Effort Estimation

Cited by 54 publications

References 30 publications

Spectral Clustering Effect in Software Development Effort Estimation

Spectral Clustering Effect in Software Development Effort Estimation

Handling of Categorical Data in Software Development Effort Estimation: A Systematic Mapping Study

The Concept of Data Mining

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