Stacking regularization in analogy-based software effort estimation

Kaushik, Anupama; Kaur, Prabhjot; Choudhary, Nisha; Choudhary, Priyanka

doi:10.1007/s00500-021-06564-w

Cited by 12 publications

(7 citation statements)

References 40 publications

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“…This leads to feature selection and coefficient shrinkage, making it suitable for high-dimensional datasets with correlated predictors. λ 1 and λ 2 are regularization parameters controlling the strength of the LASSO and Ridge penalties, respectively [56].…”

Section: Lasso (Least Absolute Shrinkage and Selection Operator)mentioning

confidence: 99%

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Rahman,

Sarwar,

Kader

et al. 2024

IEEE Access

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The average software company spends a huge amount of its revenue on R&D for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation pose significant challenges in software development, necessitating continuous improvement in estimation techniques. This study reviews recent machine learning approaches exploited to enhance software effort estimation (SEE) accuracy, focusing on research published between 2020 and 2023. The literature review employed an approach to identify pertinent research on machine learning techniques for software estimation efforts. Additionally, comparative experiments were conducted employing five commonly used ML methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and LASSO Regression. These techniques were assessed using five widely employed accuracy metrics such as Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) on seven benchmark datasets (Albrecht, Desharnais, China, Kemerer, Mayazaki94, Maxwell, COCOMO). By carefully reviewing study quality, analyzing results across the literature, and rigorously evaluating experimental outcomes, clear conclusions were drawn about the most promising techniques for achieving state-of-the-art accuracy in estimating software effort. This study makes three key contributions to the field: firstly, it furnishes a thorough overview of recent machine learning research in software effort estimation (SEE); secondly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation; and thirdly, it demonstrates the performance of publicly available datasets through experimental analysis. Enhanced estimation supports the development of better predictive models for software project time, cost, and staffing needs. The findings aim to focus future research directions and tool development toward the most accurate machine learning approaches for modeling software development effort, costs, and delivery schedules.

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Section: Lasso (Least Absolute Shrinkage and Selection Operator)mentioning

confidence: 99%

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Rahman,

Sarwar,

Kader

et al. 2024

IEEE Access

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“…KNN has been chosen in this study since it is one of the simplest estimation method which is perceived as similar to human based expert-judgement [53], [54]. KNN is a case based reasoning (CBR) or analogy based estimation (ABE) approach which assumes that a new project (which is characterized by a set of n features) will most likely have similar effort as to its similar past projects from the case base [2], [53]. A distance function is used to identify the similarity between projects on the basis of values of n features.…”

Section: ) Ridge Regression (Ridge)mentioning

confidence: 99%

“…The need to have utmost accuracy in estimated effort has become reign supreme for software development industry to better support the decision making process. Both overestimation and underestimation of required effort are highly undesirable and occurrence of these can cause failure of a software project and resource wastage [1], [2]. To achieve accurate effort estimates for the proposed software project, it is required to build SDEE model using historical projects' datasets.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

Gautam

Singh

2022

IEEE Access

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The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement. In this research, we specially investigated the effectiveness of outlier removal to improve estimation performance of 5 machine learning (ML) methods (Support Vector Regression, Random Forest, Ridge Regression, K-Nearest Neighbor, and Gradient Boosting Machines) for software development effort estimation (SDEE). We propose a novel discretization method based on Golden Section (dubbed as Golden Section based Adaptive Discretization, GSAD) to identify optimal number of outliers for SDEE dataset. The results signify the importance of optimal number of outliers' removal to improve estimations. Moreover, the results obtained after applying GSAD technique have been compared with IQR and Cooks' distance based outlier identification methods over 4 datasets: ISBSG Release 2021, UCP, NASA93 and China. The empirical results confirm that the performance of ML based SDEE methods is generally improving by employing GSAD and the proposed GSAD method has the ability to compete with the other prevalent outlier identification methods.INDEX TERMS software development effort estimation, machine learning, discretization, outlier identification, golden section method.

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“…Sebelum suatu perangkat lunak dibangun atau dikode diperlukan adanya tahapan estimasi yang digunakan untuk mengetahui effort atau upaya yang dibutuhkan dalam pengembangan perangkat lunak tersebut [1]. Pendekatan sistematis dalam estimasi pengukuran perangkat lunak menjadi penting untuk manajemen dan perencanaan perangkat lunak [2], [3]. Estimasi yang dimaksud dijadikan dasar oleh manager proyek perangkat lunak untuk mengetahui scope (besaran), biaya, jangka waktu serta personil yang dibutuhkan [4], [5].…”

Section: Pendahuluanunclassified

“…Estimasi biaya yang akurat mengarah ke proyek yang sukses, dan estimasi yang tidak akurat menghasilkan kegagalan proyek [3]. Ada beberapa teknik atau metode yang menawarkan cara untuk estimasi pengembangan perangkat lunak seperti Analogy Based Estimation (ABE) yang menggunakan model regresi berganda dan teknik penilaian dari para ahli [3], Case Based Reasoning yang didasari dengan asumsi setiap proyek perangkat lunak yang memiliki jumlah effort yang sama [6], Function Point yang dihitung dengan besaran suatu fungsional perangkat lunak [7], [8], COCOMO II yang menggunakan UFP, Tabel Scale Driver, Table Effort Mulplier [9], [10]. Dari berbagai penelitiaan yang sudah dilakukan masih ada kecenderungan yang bersifat subjektif dan adanya ketidakpastian karena tidak adanya standar perhitungan yang pasti [11].…”

Section: Pendahuluanunclassified

Estimasi Pengembangan Perangkat Lunak Dengan Use Case Size Point

Jayadi,

Juwari,

Muh.Nur Luthfi Azis

et al. 2022

bit

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In software development activities one of the stages that need to be known by the project manager and the client is the estimated effort. The estimation activity is used as a reference in determining the time and personnel involved in software development. There are several methods used in software estimation such as Function Point, COCOMO, and Use Case Point. However, there are still some shortcomings such as the subjective tendency to measure complexity and lack of calculation on technical factors and environmental factors. The estimation used in this study uses the Use Case Size Point method which is the development of the Use Case Point method. The results of the Use Case size Point were used to provide a more accurate evaluation of effort with 0.01 Mean Magnitude of Relative Error (MMRE) and 0.01 Mean Magnitude of Relative Error (MMER)

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Stacking regularization in analogy-based software effort estimation

Cited by 12 publications

References 40 publications

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

Estimasi Pengembangan Perangkat Lunak Dengan Use Case Size Point

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