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.

Due to the unavoidable bugs appearing in the most of the software systems, bug resolution has become one of the most important activities in software maintenance. To decrease the time cost in manual work, text classification techniques are applied to automatically identify severity of bug reports. In this paper, we address the problem of low-quality and class imbalance for identifying the severity of bug reports. First, we combine feature selection with instance selection to simultaneously reduce the bug report dimension and the word dimension, which could get small-scale and high-quality reduced data set. Then, an improve random oversampling technique, named, RSMOTE, which is presented to weaken the imbalancedness degree of class distribution. Finally, to avoid the random over-sampling uncertainty of RSMOTE, we develop an ensemble learning algorithm, which is based on Choquet fuzzy integral, to combine multiple RSMOTE. We empirically investigate the performance of data reduction on ten data sets of three large open source projects, namely, Eclipse, Mozilla, and GNOME. The results show that our approach can effectively reduce the data scale and improve the performance of identifying the severity of bug reports.

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.

Broad Learning System (BLS) is an emerging learning algorithm for the connectionist models, which have enjoyed much popularity on many applications. As an alternative approach of learning in deep structure, the BLS develops an incremental learning neural network that can be modeled in a flexible way, and becomes a promising technique in the field of knowledge discovery and data engineering. To further improve the performance of BLS, our focus is to investigate these algorithms who can enhance the BLS. On one hand, from the viewpoint of feature engineering, unsupervised group-wise encoding is conducted for feature extraction, and broadly-fused feature representation is used to improve the ability of BLS, in terms of the learning and reusing multi-level features. On the other hand, for imbalanced learning from disproportionate size of categories instances, a cost-sensitive BLS framework is proposed in our study, which aims to minimize the total misclassifying cost in classification learning. Last, we conduct extensive experiments on a wide range of datasets (e.g., computer vision and bug reports) to demonstrate the effectiveness of the proposed BLS framework.

Software defect prediction refers to the systematic analysis and review of software using various approaches and tools to identify potential defects or errors. Software defect prediction aids developers in swiftly identifying defects and optimizing development resource allocation, thus enhancing software quality and reliability. Previous defect prediction approaches still face two main limitations: 1) lacking of contextual semantic information and 2) Ignoring the joint reasoning between different granularities of defect predictions. In response to these challenges, we propose LineDef, a line-level defect prediction approach by capturing code contexts with graph convolutional networks. Specifically, LineDef comprises three components: the token embedding component, the graph extraction component, and the multi-granularity defect prediction component. The token embedding component maps each token to a vector to obtain a high-dimensional semantic feature representation of the token. Subsequently, the graph extraction component utilizes a sliding window to extract line-level and token-level graphs, addressing the challenge of capturing contextual semantic relationships in the code. Finally, the multi-granularity defect prediction component leverages graph convolutional layers and attention mechanisms to acquire prediction labels and risk scores, thereby achieving file-level and line-level defect prediction. Experimental studies on 32 datasets across 9 different software projects show that LineDef exhibits significantly enhanced balanced accuracy, ranging from 15.61% to 45.20%, compared to state-of-the-art file-level defect prediction approaches, and a remarkable cost-effectiveness improvement ranging from 15.32% to 278%, compared to state-of-the-art line-level defect prediction approaches. These results demonstrate that LineDef approach can extract more comprehensive information from lines of code for defect prediction.

Low-code development platforms (LCDPs) are increasingly being introduced and leveraged by major IT enterprises to lower the threshold and promote the efficiency of software development. Like other software systems, LCDPs are also inevitable to have bugs. The bugs in LCDPs may cause unpredictable consequences as they pose risks to all the downstream software products. However, to the best of our knowledge, there exist no studies that ever consider the bugs caused by LCDPs. To handle the LCDP bugs better, in this article, we conduct an empirical study of the characteristics of LCDP bugs by examining 974 confirmed bugs of four dominant LCDPs (i.e., OutSystems, Mendix, Appsmith, and Budibase) from both commercial and open-source domains. These bugs are analyzed from three perspectives, including bug root causes, bug symptoms, and the affected stages of LCDPs. Based on the analysis, we obtain a series of valuable findings. For example, around 60% of the bugs reside in the stage of designing and specifying the developed applications. Over 37% of the bugs lead LCDPs to behave unexpectedly but without showing explicit signs. Moreover, the bugs relevant to the incorrect graphics of user interfaces are significant due to the characteristics of LCDPs. These findings point out the guidelines, challenges, and future directions to address LCDP bugs.