Abstract Previous research have utilized public software defect datasets such as NASA, RELINK, and SOFTLAB, which only contain class label information. Most effort‐aware defect prediction (EADP) studies are carried out around these datasets. However, EADP studies typically relying on predicted bug number (i.e., considering modules as effort) or density (i.e., considering lines of code as effort) for ranking software modules. To explore the impact of bug number information in constructing EADP models, we access the performance degradation of the best‐performing learning‐to‐rank methods when using class labels instead of bug numbers for training. The experimental results show that using class labels instead of bug numbers in building EADP models results in an decrease in the detected bugs when module is considering as effort. When effort is LOC, using class labels to construct EADP models can lead to a significant increase in the initial false alarms and a significant increase in the modules that need to be inspected. Therefore, we recommend not only the class labels but also the bug number information should be disclosed when publishing software defect datasets, in order to construct more accurate EADP models.

Sketching is a common way to depict design ideas freely. Augmented reality technology extends the human–computer interaction space from the screen to the real world, providing a new application space for 3D sketching. However, due to the difficulty of depth perception, it is difficult for sketching strokes to maintain good planar and spatial consistency, making drawing 3D sketches in AR challenging. In this article, we propose FMASketch—a method of drawing mid-air sketches similar to building blocks. The auxiliary surfaces can ensure that the strokes drawn by users can always stay on the same plane as drawing in 2D space. Gestures are used to stitch together multiple drawing planes, and the strokes on the planes form a 3D sketch, which is very similar to how 3D modeling is done on the desktop by drawing different views and assembling them. Experiments have verified the effectiveness of the auxiliary surface generation method and gesture operations in this article. The mid-air sketching method is easy to learn and provides a new idea for rapid modeling in AR scenarios.

In the context of limited maintenance resources, predicting the severity of code smells is more practically useful than simply detecting them. Fontana et al. first empirically investigated some classification algorithms and some regression algorithms, for severity prediction. Their results showed that random forest and decision tree performed well on Mean Absolute Error (MAE), Mean Squared Error (MSE), and Spearman and Kendall rank correlation coefficients. However, they did not consider the issue of imbalanced data distribution in the severity dataset, and used inappropriate performance evaluation metrics. Therefore, we revisit the effectiveness of 10 classification methods and 11 regression methods, for code severity prediction using Cumulative Lift Chart (CLC) and Severity@20% as the primary performance metrics and Accuracy as the secondary performance indicator. The results show that the Gradient Boosting Regression (GBR) method performs the best in terms of these metrics.

Previous research have utilized public software defect datasets such as NASA, RELINK, and SOFTLAB, which only contain class label information. Almost all Effort-Aware Defect Prediction (EADP) studies are carried out around these datasets. However, EADP studies typically relying on bug density (i.e., the ratio between bug numbers and the lines of code) for ranking software modules. In order to investigate the impact of neglecting bug number information in software defect datasets on the performance of EADP models, we examine the performance degradation of the best-performing learning to rank methods when class labels are utilized instead of bug numbers. The experimental results show that neglecting bug number information in building EADP models results in an increase in the detected bugs. However, it also leads to a significant increase in the initial false alarms, ranging from 45.5% to 90.9% of the datasets, and an significant increase in the modules that need to be inspected, ranging from 5.2% to 70.4%. Therefore, we recommend not only the class labels but also the bug number information should be disclosed when publishing software defect datasets, in order to construct more accurate EADP models.

Abstract Effort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software modules to build a model to rank within‐project software modules based on the defect density, has been suggested to allocate limited testing resource efficiently. Recently, Ni et al. proposed an EACPDP method called EASC, which used all cross‐project modules to train a model without considering the data distribution difference between cross‐project and within‐project data. In addition, Ni et al. employed the different defect density calculation strategies when comparing EASC and baseline methods. To explore the effective defect density calculation strategies and methods on EACPDP, the authors compare four data filtering methods and five transfer learning methods with EASC using four commonly used defect density calculation strategies. The authors use three classification evaluation metrics and seven effort‐aware metrics to assess the performance of methods on 11 PROMISE datasets comprehensively. The results show that (1) The classification before sorting (CBS+) defect density calculation strategy achieves the best overall performance. (2) Using balanced distribution adaption (BDA) and joint distribution adaptation (JDA) with the K‐nearest neighbour classifier to build the EACPDP model can find 15% and 14.3% more defective modules and 11.6% and 8.9% more defects while achieving the acceptable initial false alarms (IFA). (3) Better comprehensive classification performance of the methods can bring better EACPDP performance to some extent. (4) A flexible adjustment of the defect threshold λ of the CBS+ strategy contribute to different goals. In summary, the authors recommend researchers and practitioners use to BDA and JDA with the CBS+ strategy to build the EACPDP model.