Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

G, Priya Varshini A; K, Anitha Kumari; Varadarajan, Vijayakumar

doi:10.3390/electronics10101195

Cited by 39 publications

(16 citation statements)

References 26 publications

(18 reference statements)

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“…A regression-based method according to ensemble learning is called a random forest (RF) 47 . Many regression trees that have grown to their maximum size without pruning make up the algorithm.…”

Section: Methodsmentioning

confidence: 99%

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Rafiee,

Jahangiri-rad,

Mohseni-Bandpei

et al. 2024

Sci Rep

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Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

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Section: Methodsmentioning

confidence: 99%

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Rafiee,

Jahangiri-rad,

Mohseni-Bandpei

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…RF implementation involves two major strategies: (1) Forming a certain number of trees with data using different bootstrap samples (i.e., bootstrap with replacement). ( 2) For splitting each node, randomly chosen best feature among the "subset of predictors" is used, instead of taking best split among all variable for splitting each node, like standard trees [44][45][46]. This study implements RF working as follows:…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

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Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources. Machine learning (ML) algorithms are largely contributing in SDEE domain, particularly ensemble effort estimation (EEE) works well in rectifying bias and subjectivity to solo ML learners. Performance of EEE significantly depends on hyperparameter composition as well as weight assignment mechanism of solo learners. However, in EEE domain, impact of optimization in terms of hyperparameter tunning as well as weight assignment is explored by few researchers. This study aims in improving SDEE performance by incorporating metaheuristic hyperparameter and weight optimization in EEE, which enables accuracy and diversity to the ensemble model. The study proposed Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE) approach. This is achieved by proposed search space division and hyperparameter optimization method named as Multi-dimensional bagging (Mdb). Metaheuristic algorithm considered for this work is Firefly algorithm (FFA), to get best hyperparameters of three base ML algorithms (Random Forest, Support vector machine and Deep Neural network) since FFA has shown promising results of fitness in terms of MAE. Further enhancement in performance is achieved by incorporating FFA-based weight optimization to construct Metaheuristic-optimized weighted ensemble (MoWE) of individual multi-dimensional bagging schemes. Proposed scheme is implemented on eight frequently utilized effort estimation datasets and results are evaluated by 5 error metrices (MAE, RMSE, MMRE, MdMRE, Pred), standard accuracy and effect size along with Wilcox statistical test. Findings confirmed that the use of FFA optimization for hyperparameter (with search space sub-division) and for ensemble weights, has significantly enhanced performance in comparison with individual base algorithms as well as other homogeneous and heterogenous EEE techniques.

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“…Software development effort estimation is one of the most popular AI techniques used by intelligent software engineering [62], and cost estimation is one of the most crucial software engineering tasks [63]. The different machine-learning algorithms include random forests [64], differential evolution [65], or extreme learning [59]. AI-based effort and cost estimation are used in traditional [66] and agile environments [60].…”

Section: Machine Learning In Fault/defect Prediction and Effort Estim...mentioning

confidence: 99%

The Use of AI in Software Engineering: A Synthetic Knowledge Synthesis of the Recent Research Literature

Kokol

2024

Information

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Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers.

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Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Cited by 39 publications

References 26 publications

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy

The Use of AI in Software Engineering: A Synthetic Knowledge Synthesis of the Recent Research Literature

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