32,528 research outputs found
Connecting Software Metrics across Versions to Predict Defects
Accurate software defect prediction could help software practitioners
allocate test resources to defect-prone modules effectively and efficiently. In
the last decades, much effort has been devoted to build accurate defect
prediction models, including developing quality defect predictors and modeling
techniques. However, current widely used defect predictors such as code metrics
and process metrics could not well describe how software modules change over
the project evolution, which we believe is important for defect prediction. In
order to deal with this problem, in this paper, we propose to use the
Historical Version Sequence of Metrics (HVSM) in continuous software versions
as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
a popular modeling technique, to take HVSM as the input to build software
prediction models. The experimental results show that, in most cases, the
proposed HVSM-based RNN model has a significantly better effort-aware ranking
effectiveness than the commonly used baseline models
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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
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Assessing Asymmetric Fault-Tolerant Software
The most popular forms of fault tolerance against design faults use "asymmetric" architectures in which a "primary" part performs the computation and a "secondary" part is in charge of detecting errors and performing some kind of error processing and recovery. In contrast, the most studied forms of software fault tolerance are "symmetric" ones, e.g. N-version programming. The latter are often controversial, the former are not. We discuss how to assess the dependability gains achieved by these methods. Substantial difficulties have been shown to exist for symmetric schemes, but we show that the same difficulties affect asymmetric schemes. Indeed, the latter present somewhat subtler problems. In both cases, to predict the dependability of the fault-tolerant system it is not enough to know the dependability of the individual components. We extend to asymmetric architectures the style of probabilistic modeling that has been useful for describing the dependability of "symmetric" architectures, to highlight factors that complicate the assessment. In the light of these models, we finally discuss fault injection approaches to estimating coverage factors. We highlight the limits of what can be predicted and some useful research directions towards clarifying and extending the range of situations in which estimates of coverage of fault tolerance mechanisms can be trusted
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