ASKDetector: An AST-Semantic and Key Features Fusion based Code Comment Mismatch Detector

Code comments can help in program comprehension and are considered as important artifacts to help developers in software maintenance. However, the comments are mostly missing or are outdated, specially in complex software projects. As a result, several automatic comment generation models are developed as a solution. The recent models explore the integration of external knowledge resources such as Unified Modeling Language class diagrams to improve the generated comments. In this paper, we propose API2Com, a model that leverages the Application Programming Interface Documentations (API Docs) as a knowledge resource for comment generation. The API Docs include the description of the methods in more details and therefore, can provide better context in the generated comments. The API Docs are used along with the code snippets and Abstract Syntax Trees in our model. We apply the model on a large Java dataset of over 130,000 methods and evaluate it using both Transformer and RNN-base architectures. Interestingly, when API Docs are used, the performance increase is negligible. We therefore run different experiments to reason about the results. For methods that only contain one API, adding API Docs improves the results by 4% BLEU score on average (BLEU score is an automatic evaluation metric used in machine translation). However, as the number of APIs that are used in a method increases, the performance of the model in generating comments decreases due to long documentations used in the input. Our results confirm that the API Docs can be useful in generating better comments, but, new techniques are required to identify the most informative ones in a method rather than using all documentations simultaneously.

Code comments are one of the effective means for assisting programmers to understand the source code. High-quality code comments play an important role in areas such as software maintenance and software reuse. Good code comments can help programmers understand the role of source code and facilitate comprehension of programs and software maintenance tasks quickly. But in reality, most programmers only pay attention to the code and ignore comments and documents, which greatly reduce the program's readability and maintainability. This paper has compiled the relevant research on code comments so far, mainly including four aspects: automatic generation of code comments, consistency of code comments, classification of code comments, and quality evaluation of code comments. By analyzing relevant methods in this research field, it provides more complete information for future research.

Internet code search is quite popular research area. StackOverflow allows developers to ask and answer questions about code. Previous approach to search code on StackOverflow uses tf-idf method that based on number of occurrences of words to recommend source code. This method has the disadvantage that variable or method identifiers are considered as normal words, even though identifiers are often a combination of two or more words. For example, there is an identifier named “randomString”. In that case, if we search using a keyword “random” the system probably will not recommend “randomString” because both words are different. Concept location can tackle this problem. Concept location has been used widely to obtain the correlation between code with a specific concepts or features. Previous research of concept location only focused on source code's comments, and relation among the objects within the source code. This research proposes a mechanism for finding code on StackOverflow uses Latent Dirichlet Allocation (LDA) using concept location in the preprocessing stage. Questions, answers, and code snippets about Java programming are downloaded from StackOverflow to a local repository. Corpuses are generated by extracting questions, answers and code snippets. Inferencing concept location from source code is created using LDA algorithm. Developers query concepts and then system will recommend source code based on the relevant concepts. The result of the experiment shows that the system is able to recommend source code with 48% average of precision and 58% average of recall.

Code comment generation typically refers to the process of generating concise natural language descriptions for a piece of code, which facilitates program comprehension activities. Inline code comments, as a part of code comments, are also crucial for program comprehension. Recently, the emergence of large language models (LLMs) has significantly boosted the performance of natural language processing tasks. This naturally inspires us to explore the performance of the LLMs in the task of inline code comment generation. To this end, we evaluate open-source LLMs on a large-scale dataset and compare the results with the current state-of-the-art methods. Specifically, we explore the model performance in the following scenarios based on the widely used evaluation metrics (i.e. BLEU, Meteor, and ROUGE-L): (1) generation with simple instruction; (2) few-shot-guided generation with random examples selected from the database; (3) few-shot-guided generation with similar examples selected from the database; and (4) adopt the re-ranking strategy for the output of LLMs. Our findings reveal that: (1) under the simple instruction scenario, LLMs could not fully show the potential in the task of inline comment generation compared to the state-of-the-art models; (2) random few-shot leads to a slight improvement; (3) similar few-shot and re-ranking strategy could significantly enhance the performance of LLMs; and (4) for inline comment and code snippet pairs with different intents, why category achieves the best performance and what category achieves relatively poorer performance. That remains consistent across all four scenarios. Our findings shed light on future research directions for using LLMs in inline comment generation tasks.

Insider threat is destructive and concealable, making addressing it a challenging task in cybersecurity. Most existing methods transform user behavior into sequential information and analyze user behavior while neglecting structural information among users, resulting in high false positives. To solve this problem, in this paper, we propose Dual-Domain Graph Convolutional Network (referred to as DD-GCN), a graph-based modularized method for high accuracy and adaptive insider threat detection. The central idea is to convert user features and structural information into heterogeneous graphs in the light of various relationships and take user behavior and relationship into account together. To this end, a weighted feature similarity mechanism is applied to balance the feature similarity of users and original linkages among them so as to generate the fused structure. Next, specific graph embeddings are extracted from the original topology structure and fused structure simultaneously, which convert behavior information into high-level representations. Furthermore, an attention mechanism is applied to learn the adaptive importance weights of the user's features in the corresponding embedding. The combination and difference constraints are proposed to enhance the learned embeddings' commonality and the ability to capture different information. Extensive experiments on two real-world datasets clearly show that our proposed DD-GCN extracts the most correlated information from structural topology and feature information substantially, and achieves improved accuracy with a clear margin.

With advancements in software development and artificial intelligence, defect prediction has gradually become an essential component of the software development lifecycle. Historically, defect prediction has been considered a multiclass classification problem because defect classes are mutually exclusive. However, software defects can belong to multiple categories simultaneously, making multilabel classification a more appropriate approach. A defect report typically contains the title, body, comments from developers and testers, and code snippets. We used these data items and performed data wrangling on these data to create a holistic summary of the defect report, which contains all the vital information that is useful for defect predictions. In this study, we investigated the multilabel dimension of the defect and performed multilabel classification using machine learning and deep learning techniques while considering the class imbalance and correlations between the labels. In the traditional classification methods, we used three classifiers: Multinomial Naive Bayes, Logistic Regression, and Random Forest. Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) with Classifier Chains are used in deep learning. To check the dataset quality, appropriate feature selection, and data dimensionality reduction, we used the chi-square test. To handle the class imbalance, we used Non-Negative Least Squares (NNLS). Our experimental investigations showed significant improvements in the model performance across machine learning and deep learning once the dataset was balanced before training the models. Visual plots of evaluation metrics, such as Hamming loss, Recall, Precision, and F1-score, clearly demonstrated the analysis outcome.

Currently, developers are often obligated to enhance code quality. High-quality code is often accompanied with comprehensive summaries, including code documentation and function explanations, which are invaluable for maintenance and further development. Regrettably, few software projects provide sufficient code comments owing to the high costs associated with human labeling. Contemporary researchers in software engineering concentrate on the methods for automated comment generating. Initial algorithms depended on handwritten templates or information retrieval methods. With the advancement of machine learning, researchers construct automated models for machine translation instead. Nonetheless, the produced code comments remain inadequate owing to the significant disparity between code structure and normal language. This study introduces a unique deep learning model, At-ComGen, which utilizes hybrid attention for the automated creation of source code comments. Utilizing two separate LSTM encoders, our approach integrates essential tokens from source code functions with the code structure, represented by a corresponding Abstract Syntax Tree. In contrast to earlier data-driven models, our methodology utilizes code syntax and semantics in the generation of comments. The hybrid attention method, used for comment creation for the first time to our knowledge, enhances the quality of code comments. The tests demonstrate that At-ComGen is efficacious and surpasses other prevalent methodologies. Machine comments from Seq2Seq and CODE-NN disregard code structure underlying DeepCom and At-ComGen. At-ComGen has 59.3%, 36.4%, 43.3%, and 13.1% higher comment BLEU values than baseline models for a 5-line function. Even though model performance reduces with comment length, At-ComGen's comments often outperform others. 5–10-word machine comments work best. For reference length 10, At-ComGen has 38.2%, 23.7%, 9.3%, and 4.4% greater BLEU values than the other baseline models.

Code comments are valuable for program comprehension and software maintenance. However, comments can be inconsistent or out-of-date after code changes. To tackle this problem, Just-In-Time (JIT) comment updating aims to automatically update comments with code changes. Existing approaches for this task use edit sequences of source code to model code changes. Meanwhile, recent researches indicate that neural models based on abstract syntax trees (AST) can help represent source code. In this paper, we propose a new method to learn code changes by combining code edit sequences with AST edit sequences, so that the generated new comment can be more accurate. Our approach utilizes three encoders to encode code edit sequences, AST edit sequences and old comment token sequences, respectively. The outputs of the encoders are then decoded into a sequence of edit actions, which is parsed to generate a new comment. The proposed method is evaluated on a public dataset using seven metrics, and the experimental results show that our approach outperforms the baselines. Furthermore, when the new comment has a larger edit distance than the old one, our model shows better performance.

GAC-SleepNet: A dual-structured sleep staging method based on graph structure and Euclidean structure

MotivationProtein representation learning methods have shown great potential to many downstream tasks in biological applications. A few recent studies have demonstrated that the self-supervised learning is a promising solution to addressing insufficient labels of proteins, which is a major obstacle to effective protein representation learning. However, existing protein representation learning is usually pretrained on protein sequences without considering the important protein structural information.ResultsIn this work, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a graph neural network model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed graph neural network model via a novel pseudo bi-level optimization scheme. We conduct experiments on three downstream tasks: the binary classification into membrane/non-membrane proteins, the location classification into 10 cellular compartments, and the enzyme-catalyzed reaction classification into 384 EC numbers, and these experiments verify the effectiveness of our proposed method.Availability and implementationThe Alphafold2 database is available in https://alphafold.ebi.ac.uk/. The PDB files are available in https://www.rcsb.org/. The downstream tasks are available in https://github.com/phermosilla/IEConv\\_proteins/tree/master/Datasets. The code of the proposed method is available in https://github.com/GGchen1997/STEPS_Bioinformatics.

In this paper, we propose a novel sequential neural network with structure attention to model information diffusion. The proposed model explores both sequential nature of an information diffusion process and structural characteristics of user connection graph. The recurrent neural network framework is employed to model the sequential information. The attention mechanism is incorporated to capture the structural dependency among users, which is defined as the diffusion context of a user. A gating mechanism is further developed to effectively integrate the sequential and structural information. The proposed model is evaluated on the diffusion prediction task. The performances on both synthetic and real datasets demonstrate its superiority over popular baselines and state-of-the-art sequence-based models.

Proposed is an efficient face recognition algorithm using the discrete cosine transform DCT Technique for reducing dimensionality and image parameterization. These DCT coefficients are examined by a MLP (Multi-Layer Perceptron) and radial basis function RBF neural networks. Their purpose is to present a face recognition system that is a combination of discrete cosine transform (DCT) algorithm with a MLP and RBF neural networks. Neural networks have been widely applied in pattern recognition for the reason that neural-networks-based classifiers can incorporate both statistical and structural information and achieve better performance than the simple minimum distance classifiers. The authors demonstrate experimentally that when DCT coefficients are fed into a back propagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Comparison with other statistical methods like Principal component Analysis (PCA) and Linear Discriminant Analysis (LDA) is presented. Their face recognition system is tested on the computer vision science research projects and the ORL database.

How to identify class comment types? A multi-language approach for class comment classification

Inspired by the state-of-the-art performance of MLP-Mixer on image classification task, multilayer perceptron (MLP) model attracts a number of researcher's attention again. Although various MLP architectures have been proposed, most of them focus on image classification domain. In this paper, we extend MLP to the task of image generation based on generative adversarial network (GAN). The pure MLP model is not friendly to small dataset because it is a data-hungry architecture. Thus, we leverage a hybrid model to solve the problem which uses MLP blocks as generator and CNN blocks as discriminator. Experimental results demonstrate that our model outperforms the pure CNN network on CIFAR- 10 dataset in view of two different evaluation metrics. Besides, we apply eight widely popular MLP structures to our generator to validate which one achieves the most excellent performance in image generation task.

With the enhancement of hardware and software engineering, the effectiveness and correctness of software is less and less doubted and customers are more aware about whether software services are available or not when needed. Software maintenance is one of the main reasons that make software unavailable and it is often very expensive to perform maintenance tasks. Common approaches of studying software maintenance are to consider it as a static by‐product of software operation and only the maintenance cost is covered. In this paper, software maintenance policies are studied with the consideration of unavailable service time. A non‐homogeneous continuous Markov chain is adopted for modeling the software operation and maintenance process, and the cost of software unavailability that is brought in by software maintenance is investigated and analyzed for searching the optimal maintenance policy, which aims at minimizing the average maintenance time cost. The optimality of our proposed policy is shown and checked by numerical examples with discussions of its possible application perspectives. Copyright © 2010 John Wiley & Sons, Ltd.