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Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner's cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships. (ii) We utilized latent Dirichlet allocation for tag modeling, assigning tag categories to newly posted questions and ranking experts related to specific tags for active questioners based on hub and authority scores. (iii) We enhanced recommendation accuracy in the TDNR model by introducing a degree of trust between questioners and responders. (iv) We incorporated the questioner-responder relational graph, derived from structural preference information, into our proposed model. We evaluated the proposed model on the Stack Overflow dataset using mean absolute precision (MAP), root mean squared error (RMSE), and F-measure metrics. Our significant findings are that TDNR is a hybrid approach that provides more accurate recommendations compared to rating-based and social-trust-based approaches, the proposed model can facilitate the formation of informal e-learning communities, and experiments show that TDNR outperforms the competing methods by an improved margin. The model's robustness, demonstrated by superior MAE, RMSE, and F-measure metrics, makes it a reliable solution for addressing information overload and user sparsity in Stack Overflow. By accurately modeling complex relationships and incorporating trust degrees, TDNR provides more relevant and personalized recommendations, even in cold-start scenarios. This enhances user experience by facilitating the formation of supportive learning communities and ensuring new learners receive accurate recommendations. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for automatically classifying questions with multiple labels, using data specifically sourced from Stack Overflow. Traditional tagging methods frequently face challenges due to the complexity and semantic diversity of these questions, resulting in inconsistent and sometimes inaccurate results. The process starts with preprocessing to remove any unwanted elements. Next, we convert the questions into meaningful representations using SMPNet. The semantic vectors obtained are then processed using UMAP to help us understand the overall structure of the data and make it easier to cluster similar items. After dimensionality reduction with UMAP, we use the K-Means method to group the questions into clusters, with the best number of groups determined by the Silhouette Score. Finally, a fine-tuned DeBERTa model is trained for each cluster to accurately predict the appropriate tags. Our approach significantly outperforms traditional methods, achieving 2% improvement over the best baseline. This strategy improves model efficiency by narrowing the focus to specific subsets of data.

As the predominant modern version control system, Git has become an indispensable tool for both commercial and open‐source software projects. It substantially improves software development effectiveness and efficiency through its distributed version control system, fostering seamless collaboration among teams and across locations. However, research has found that many developers have doubts about using Git commands, while the official Git documentation is rather scanty, that is, lacking sufficient explanations and examples. To help developers learn and use Git commands, we propose the first approach (Richen) for enriching Git documentation with usage examples and scenarios by leveraging crowd knowledge from Stack Overflow. Richen retrieves Git‐related posts from Stack Overflow, extracts relevant Q&A pairs, and selects representative command usages, including usage examples and scenarios, for different Git commands. Experimental results have shown that Richen can extract informative and concise command usages for Git commands. Compared with alternative methods adapted from API usage mining, the command usages obtained by Richen have significant advantages in terms of relevance, readability, and usability. Furthermore, we have shown through an empirical study that the command usages extracted by Richen can better help developers complete Git command‐related tasks. [ABSTRACT FROM AUTHOR]

Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner’s cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships. (ii) We utilized latent Dirichlet allocation for tag modeling, assigning tag categories to newly posted questions and ranking experts related to specific tags for active questioners based on hub and authority scores. (iii) We enhanced recommendation accuracy in the TDNR model by introducing a degree of trust between questioners and responders. (iv) We incorporated the questioner-responder relational graph, derived from structural preference information, into our proposed model. We evaluated the proposed model on the Stack Overflow dataset using mean absolute precision (MAP), root mean squared error (RMSE), and F-measure metrics. Our significant findings are that TDNR is a hybrid approach that provides more accurate recommendations compared to rating-based and social-trust-based approaches, the proposed model can facilitate the formation of informal e-learning communities, and experiments show that TDNR outperforms the competing methods by an improved margin. The model’s robustness, demonstrated by superior MAE, RMSE, and F-measure metrics, makes it a reliable solution for addressing information overload and user sparsity in Stack Overflow. By accurately modeling complex relationships and incorporating trust degrees, TDNR provides more relevant and personalized recommendations, even in cold-start scenarios. This enhances user experience by facilitating the formation of supportive learning communities and ensuring new learners receive accurate recommendations.

IntroductionRecent advances in generative Artificial Intelligence (AI) and Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) and AI-powered chatbots like ChatGPT, which have numerous practical applications. Notably, these models assist programmers with coding queries, debugging, solution suggestions, and providing guidance on software development tasks. Despite known issues with the accuracy of ChatGPT’s responses, its comprehensive and articulate language continues to attract frequent use. This indicates potential for ChatGPT to support educators and serve as a virtual tutor for students.MethodsTo explore this potential, we conducted a comprehensive analysis comparing the emotional content in responses from ChatGPT and human answers to 2000 questions sourced from Stack Overflow (SO). The emotional aspects of the answers were examined to understand how the emotional tone of AI responses compares to that of human responses.ResultsOur analysis revealed that ChatGPT’s answers are generally more positive compared to human responses. In contrast, human answers often exhibit emotions such as anger and disgust. Significant differences were observed in emotional expressions between ChatGPT and human responses, particularly in the emotions of anger, disgust, and joy. Human responses displayed a broader emotional spectrum compared to ChatGPT, suggesting greater emotional variability among humans.DiscussionThe findings highlight a distinct emotional divergence between ChatGPT and human responses, with ChatGPT exhibiting a more uniformly positive tone and humans displaying a wider range of emotions. This variance underscores the need for further research into the role of emotional content in AI and human interactions, particularly in educational contexts where emotional nuances can impact learning and communication.

The tremendous success of Stack Overflow has accumulated an extensive corpus of software engineering knowledge, thus motivating researchers to propose various solutions for analyzing its content. The performance of such solutions hinges significantly on the selection of representation models for Stack Overflow posts. As the volume of literature on Stack Overflow continues to burgeon, it highlights the need for a powerful Stack Overflow post representation model and drives researchers' interest in developing specialized representation models that can adeptly capture the intricacies of Stack Overflow posts. The state-of-the-art (SOTA) Stack Overflow post representation models are Post2Vec and BERTOverflow, which are built upon neural networks such as convolutional neural network and transformer architecture (e.g., BERT). Despite their promising results, these representation methods have not been evaluated in the same experimental setting. To fill the research gap, we first empirically compare the performance of the representation models designed specifically for Stack Overflow posts (Post2Vec and BERTOverflow) in a wide range of related tasks (i.e., tag recommendation, relatedness prediction, and API recommendation). The results show that Post2Vec cannot further improve the SOTA techniques of the considered downstream tasks, and BERTOverflow shows surprisingly poor performance. To find more suitable representation models for the posts, we further explore a diverse set of transformer-based models, including (1) general domain language models (RoBERTa, Longformer, and GPT2) and (2) language models built with software engineering related textual artifacts (CodeBERT, GraphCodeBERT, seBERT, CodeT5, PLBart, and CodeGen). This exploration shows that models like CodeBERT and RoBERTa are suitable for representing Stack Overflow posts. However, it also illustrates the "No Silver Bullet" concept, as none of the models consistently wins against all the others. Inspired by the findings, we propose SOBERT, which employs a simple yet effective strategy to improve the representation models of Stack Overflow posts by continuing the pre-training phase with the textual artifact from Stack Overflow. The overall experimental results demonstrate that SOBERT can consistently outperform the considered models and increase the SOTA performance significantly for all the downstream tasks. [ABSTRACT FROM AUTHOR]

This research introduces an innovative approach for identifying duplicate questions within the Stack Overflow community, a challenging task in NLP. Leveraging deep learning techniques, our proposed methodology combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture both local and long-term dependencies in textual data. We employ word embeddings, specifically Google’s Word2Vec and GloVe, to enhance text representation. Extensive experiments on the Stack Overflow dataset demonstrate the effectiveness of our approach, achieving an impressive accuracy of 87.09% and a recall rate of 87.%. The integration of CNN and LSTM models significantly streamlines preprocessing, making it a valuable tool for detecting duplicate questions. Future directions include extending the model to multiple languages and exploring alternative word embedding techniques. Our approach presents promising applications beyond Stack Overflow, offering solutions for identifying similar questions on various QA platforms.

Technical Q&A sites, such as Stack Overflow and Ask Ubuntu, have been widely utilized by software engineers to seek support for development challenges. However, not all the raised questions get instant feedback, and the retrieved answers can vary in quality. The users can hardly avoid spending much time before solving their problems. Prior studies propose approaches to automatically recommend answers for the question posts on technical Q&A sites. However, the lengthiness and the lack of background knowledge issues limit the performance of answer recommendation on these sites. The irrelevant sentences in the posts may introduce noise to the semantics learning and prevent neural models from capturing the gist of texts. The lexical gap between question and answer posts further misleads current models to make failure recommendations. From this end, we propose a novel neural network named TopicAns for answer selection on technical Q&A sites. TopicAns aims at learning high-quality representations for the posts in Q&A sites with a neural topic model and a pre-trained model. This involves three main steps: (1) generating topic-aware representations of Q&A posts with the neural topic model, (2) incorporating the corpus-level knowledge from the neural topic model to enhance the deep representations generated by the pre-trained language model, and (3) determining the most suitable answer for a given query based on the topic-aware representation and the deep representation. Moreover, we propose a two-stage training technique to improve the stability of our model. We conduct comprehensive experiments on four benchmark datasets to verify our proposed TopicAns's effectiveness. Experiment results suggest that TopicAns consistently outperforms state-of-the-art techniques by over 30% in terms of Precision@1. [ABSTRACT FROM AUTHOR]

Introduction: Recent advances in generative Artificial Intelligence (AI) and Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) and AI-powered chatbots like ChatGPT, which have numerous practical applications. Notably, these models assist programmers with coding queries, debugging, solution suggestions, and providing guidance on software development tasks. Despite known issues with the accuracy of ChatGPT's responses, its comprehensive and articulate language continues to attract frequent use. This indicates potential for ChatGPT to support educators and serve as a virtual tutor for students., Methods: To explore this potential, we conducted a comprehensive analysis comparing the emotional content in responses from ChatGPT and human answers to 2000 questions sourced from Stack Overflow (SO). The emotional aspects of the answers were examined to understand how the emotional tone of AI responses compares to that of human responses., Results: Our analysis revealed that ChatGPT's answers are generally more positive compared to human responses. In contrast, human answers often exhibit emotions such as anger and disgust. Significant differences were observed in emotional expressions between ChatGPT and human responses, particularly in the emotions of anger, disgust, and joy. Human responses displayed a broader emotional spectrum compared to ChatGPT, suggesting greater emotional variability among humans., Discussion: The findings highlight a distinct emotional divergence between ChatGPT and human responses, with ChatGPT exhibiting a more uniformly positive tone and humans displaying a wider range of emotions. This variance underscores the need for further research into the role of emotional content in AI and human interactions, particularly in educational contexts where emotional nuances can impact learning and communication., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fatahi, Vassileva and Roy.)