Selection of Metrics for the Defect Prediction

Bluemke, Ilona; Stępień, Anna

doi:10.1007/978-3-319-39639-2_4

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The island of Crete, Greece, has a typical Mediterranean island environment, with about 53% of the annual precipitation occurring in the winter, 23% during autumn and 20% during spring, covering most irrigation requirements, while there is almost no rainfall during summer [68,69]. The average precipitation for a normal year is approximately 934 mm or 7697 Mm 3 [70], but the nonuniform distribution in space (a negative gradient of almost 300 mm from west to east and a strong orographic effect) and time (dry summers) makes dry season precipitation a very small but crucial portion of the total supply [71].…”

Section: Study Areamentioning

confidence: 99%

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

et al. 2020

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This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.

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Section: Study Areamentioning

confidence: 99%

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

et al. 2020

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“…In this context, Mean Decrease Gini (MDG) aggregates the Gini gain over all splits and trees to assess the classifying capacity of a variable (Friedman et al, 2009) and is thus a metric of the homogeneity of nodes and leaves in the RF (Bluemke and Stepień, 2016).…”

Section: Evaluation Criteriamentioning

confidence: 99%

Spatial Downscaling of Alien Species Presences Using Machine Learning

2017

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Spatially explicit assessments of alien species environmental and socio-economic impacts, and subsequent management interventions for their mitigation, require large scale, high-resolution data on species presence distribution. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A bootstrapping scheme is designed to account for sub-setting uncertainty, and subsets are used to train a sufficiently large number of RF models. RF results are processed to estimate variable importance and model performance. The method is tested with an ∼8 × 8 km 2 grid containing floral alien species presence and several potentially exploratory indices of climatic, habitat, land use, and soil property covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16 × 16 km 2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Uncertainty assessment of the spatial downscaling of alien species' occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution. Furthermore, the RF architecture allows for tuning toward operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

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“…Bluemke et al [39] describe the process of choosing appropriate metrics for defect prediction. Accordingly, Jiarpakdee et al [40] suggest that researchers should be aware of redundant metrics before constructing a defect prediction model to maximize their studies' internal validity.…”

Section: Software Metricmentioning

confidence: 99%

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Zhong

Song

et al. 2021

Mathematical Problems in Engineering

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Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measure and statistical methods. Then, we validate the dominant performance of CPDP with hybrid metrics. Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models. The experimental results suggest that the impact of different metric sets on the performance of CPDP is significantly distinct, with semantic and structural metrics performing better. Additionally, trials indicate that it is helpful for CPDP to increase the software’s metrics diversity appropriately, as the CPDP-OSS improvement is up to 53.8%. Finally, compared with two baseline methods, TCA+ and TDSelector, the optimized CPDP model is viable in practice, and the improvement rate is up to 50.6% and 25.7%, respectively.

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Selection of Metrics for the Defect Prediction

Cited by 5 publications

References 15 publications

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

Spatial Downscaling of Alien Species Presences Using Machine Learning

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

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