In order to improve the quality of a software system, software defect prediction aims to automatically identify defective software modules for efficient software test. To predict software defect, those classification methods with static code attributes have attracted a great deal of attention. In recent years, machine learning techniques have been applied to defect prediction. Due to the fact that there exists the similarity among different software modules, one software module can be approximately represented by a small proportion of other modules. And the representation coefficients over the pre-defined dictionary, which consists of historical software module data, are generally sparse. In this paper, we propose to use the dictionary learning technique to predict software defect. By using the characteristics of the metrics mined from the open source software, we learn multiple dictionaries (including defective module and defective-free module sub-dictionaries and the total dictionary) and sparse representation coefficients. Moreover, we take the misclassification cost issue into account because the misclassification of defective modules generally incurs much higher risk cost than that of defective-free ones. We thus propose a cost-sensitive discriminative dictionary learning (CDDL) approach for software defect classification and prediction. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that CDDL outperforms several representative state-of-the-art defect prediction methods. Categories and Subject DescriptorsD.2.9 [Management]: Software quality assurance (SQA), G.1.3 [Numerical Linear Algebra]: Sparse, structured, and very large systems (direct and iterative methods), I.5.2 [Design Methodology]: Classifier design and evaluation. General Terms Algorithms KeywordsSoftware defect prediction, dictionary learning, sparse representation, cost-sensitive discriminative dictionary learning (CDDL).

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In order to predict flow behavior and find the optimum hot working processing parameters for 5754 aluminum alloy, the experimental flow stress data obtained from the isothermal hot compression tests on a Gleeble-3500 thermo-simulation apparatus, with different strain rates (0.1–10 s–1) and temperatures (300–500 °C), were used to construct the constitutive models of the strain-compensation Arrhenius (SA) and back propagation (BP) artificial neural network (ANN). In addition, an optimized BP–ANN model based on the genetic algorithm (GA) was established. Furthermore, the predictability of the three models was evaluated by the statistical indicators, including the correlation coefficient (R) and average absolute relative error (AARE). The results showed that the R of the SA model, BP–ANN model, and ANN–GA model were 0.9918, 0.9929, and 0.9999, respectively, while the AARE of these models was found to be 3.2499–5.6774%, 0.0567–5.4436% and 0.0232–1.0485%, respectively. The prediction error of the SA model was high at 400 °C. It was more accurate to use the BP–ANN model to determine the flow behavior compared to the SA model. However, the BP–ANN model had more instability at 300 °C and a true strain in the range of 0.4–0.6. When compared with the SA model and BP–ANN model, the ANN–GA model had a more efficient and more accurate prediction ability during the whole deformation process. Furthermore, the dynamic softening characteristic was analyzed by the flow curves. All curves showed that 5754 aluminum alloy showed the typical rheological characteristics. The flow stress rose rapidly with increasing strain until it reached a peak. After this, the flow stress remained constant, which demonstrates a steady flow softening phenomenon. Besides, the flow stress and the required variables to reach the steady state deformation increased with increasing strain rate and decreasing temperature.