Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and eXplainable GPR

Jamei, Mehdi; Ali, Mumtaz; Karbasi, Masoud; Karimi, Bakhtiar; Jahannemaei, Neshat; Farooque, Aitazaz Ahsan; Yaseen, Zaher Mundher

doi:10.1016/j.eswa.2023.121512

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2023.121512

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Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and eXplainable GPR

Mehdi Jamei,

Mumtaz Ali,

Masoud Karbasi

et al.

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Cited by 14 publications

References 64 publications

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Industrial adoption of machine learning techniques for early identification of invalid bug reports

Laiq,

Ali,

Börstler

et al. 2024

Empir Software Eng

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Despite the accuracy of machine learning (ML) techniques in predicting invalid bug reports, as shown in earlier research, and the importance of early identification of invalid bug reports in software maintenance, the adoption of ML techniques for this task in industrial practice is yet to be investigated. In this study, we used a technology transfer model to guide the adoption of an ML technique at a company for the early identification of invalid bug reports. In the process, we also identify necessary conditions for adopting such techniques in practice. We followed a case study research approach with various design and analysis iterations for technology transfer activities. We collected data from bug repositories, through focus groups, a questionnaire, and a presentation and feedback session with an expert. As expected, we found that an ML technique can identify invalid bug reports with acceptable accuracy at an early stage. However, the technique’s accuracy drops over time in its operational use due to changes in the product, the used technologies, or the development organization. Such changes may require retraining the ML model. During validation, practitioners highlighted the need to understand the ML technique’s predictions to trust the predictions. We found that a visual (using a state-of-the-art ML interpretation framework) and descriptive explanation of the prediction increases the trustability of the technique compared to just presenting the results of the validity predictions. We conclude that trustability, integration with the existing toolchain, and maintaining the techniques’ accuracy over time are critical for increasing the likelihood of adoption.

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Industrial adoption of machine learning techniques for early identification of invalid bug reports

Laiq,

Ali,

Börstler

et al. 2024

Empir Software Eng

View full text Add to dashboard Cite

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Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model

Hai,

Ahmadianfar,

Halder

et al. 2024

Environ Sci Pollut Res

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An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard

Gholami,

Darvishi,

Moradi

et al. 2024

Environ Sci Pollut Res

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Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and eXplainable GPR

Cited by 14 publications

References 64 publications

Industrial adoption of machine learning techniques for early identification of invalid bug reports

Industrial adoption of machine learning techniques for early identification of invalid bug reports

Surface water quality index forecasting using multivariate complementing approach reinforced with locally weighted linear regression model

An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard

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