With the help of automated classification, severe bugs can be rapidly identified so that the latent damage to software projects can be minimized. However, bug report datasets commonly suffer from disproportionate number of category samples. When presented with the situation of class imbalance, most standard classification learning approaches fail to properly learn the distributive characteristics of the samples and tend to lead unfavorable performance to predict class label. In this case, imbalanced learning becomes critical to advance classification algorithms. In this paper, we propose an improved synthetic minority oversampling technique to avoid the degraded performance caused by class imbalance in bug report datasets. Moreover, to lessen the chance of occasionalities in random sampling process, we propose an repeated sampling technique to train different but related classifiers. Finally, an ensemble algorithm based on Choquet fuzzy integral is employed to combine the wisdom of crowds and make better decisions. We conduct comprehensive experiments on several bug report datasets from real-world bug repositories. The results demonstrate that the proposed method boosts the classification performance across the classes of the data. Specifically, compared with various ensemble learning techniques, the Choquet fuzzy integral achieves outstanding results on integrating multiple random over-sampling techniques.

To recognize individual activities in multi-resident environments with pervasive sensors, some researchers have pointed out that finding data associations can contribute to activity recognition and previous methods either need or infer data association when recognizing new multi-resident activities based on new observations from sensors. However, it is often difficult to find out data associations, and available approaches to multi-resident activity recognition degrade when the data association is not given or induced with low accuracy. This paper exploits some simple knowledge of multi-resident activities through defining Combined label and the state set, and proposes a two-stage activity recognition method for multi-resident activity recognition. We define Combined label states at the model building phase with the help of data association, and learn Combined label states at the new activity recognition phase without the help of data association. Our two stages method is embodied in the new activity recognition phase, where we figure out multi-resident activities in the second stage after learning Combined label states at first stage. The experiments using the multi-resident CASAS data demonstrate that our method can increase the recognition accuracy by approximately 10%.

The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO) algorithm to solve DVRPs, utilizing a greedy strategy. Both migration operation and the butterfly adjusting operator only accept the offspring of butterfly individuals that have better fitness than their parents. To improve performance, a later perturbation procedure is implemented, to maintain a balance between global diversification and local intensification. The computational results indicate that the proposed technique outperforms the existing approaches in the literature for average performance by at least 9.38%. In addition, 12 new best solutions were found. This shows that this proposed technique consistently produces high-quality solutions and outperforms other published heuristics for the DVRP.

Code comments are a crucial source of software documentation that captures various aspects of the code. Such comments play a vital role in understanding the source code and facilitating communication between developers. However, with the iterative release of software, software projects become larger and more complex, leading to a corresponding increase in issues such as mismatched, incomplete, or outdated code comments. These inconsistencies in code comments can misguide developers and result in potential bugs, and there has been a steady rise in reports of such inconsistencies over time. Despite numerous methods being proposed for detecting code comment inconsistencies, their learning effect remains limited due to a lack of consideration for issues such as characterization noise and labeling errors in datasets. To overcome these limitations, we propose a novel approach called MCCL that first removes noise from the dataset and then detects inconsistent code comments in a timely manner, thereby enhancing the model’s learning ability. Our proposed model facilitates better matching between code and comments, leading to improved development of software engineering projects. MCCL comprises two components, namely method comment detection and confidence learning denoising. The method comment detection component captures the intricate relationships between code and comments by learning their syntactic and semantic structures. It correlates the code and comments through an attention mechanism to identify how changes in the code affect the comments. Furthermore, confidence learning denoising component of MCCL identifies and removes characterization noises and labeling errors to enhance the quality of the datasets. This is achieved by implementing principles such as pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. By effectively eliminating noise from the dataset, our model is able to more accurately learn inconsistencies between comments and source code. Our experiments on 1,518 open-source projects demonstrate that MCCL can accurately detect inconsistencies, achieving an average F1-score of 82.6%. This result outperforms state-of-the-art methods by 2.4% to 28.0%. Therefore, MCCL is more effective in identifying inconsistent comments based on code changes compared to existing approaches.