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Due to the growing complexity of software systems, there has been a dramatic increase in research and industry demand on refactoring. Refactoring research nowadays addresses challenges beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommending specific refactoring activities, detecting refactoring opportunities and testing the correctness of applied refactorings. Very few studies focused on the challenges that practitioners face when refactoring software systems and what should be the current refactoring research focus from the developers’ perspective. Without such knowledge, tool builders invest in the wrong direction, and researchers miss many opportunities for improving the practice of refactoring. In this paper, we collected data from the popular online Q&A site, Stack Overflow, and analyzed posts to identify what do developers ask about refactoring. We clustered these questions to find the different refactoring related topics using one of the most popular topic modeling algorithms, Latent Dirichlet Allocation (LDA). We found that developers are asking about design patterns, design and user interface refactoring, web services, parallel programming, and mobile apps. We also identified what popular refactoring challenges are the most difficult and the current important topics and questions related to refactoring. Moreover, we discovered gaps between existing research on refactoring and the challenges developers face. To the best of our knowledge, this paper represents the first Stack Overflow study to identify the refactoring topics discussed by developers. Our study can help researchers to focus on practical refactoring problems, practitioners know more about current challenges and build better refactoring tools, and educators revise curriculum to target current needs on refactoring.

E-books have become more prevalent in the present day, due to their availability and numerous advantages over textbooks, and more so in the domain of computer science and programming. On the other hand, learners also use Q&A websites such as Stack Overflow as these websites provides a platform for asking and answering questions in communication with the developer community. Even though learning programming requires dynamic content, the current e-books are observed to be static in nature. To address this challenge, we propose an approach to overlay related Q&As throughout the e-book, bringing dynamic content into a static textbook. The approach consists in summarizing different parts of the text, identifying core keywords from the text, finding related core Q&As from Stack Overflow and finally integrating Q&As into the e-book. To achieve this, we take an e-book as input, apply Latent Dirichlet Allocation and generate augmented e-book with relevant Q&As. We have evaluated DynamiQue prototype with 30 volunteers in the age group of 18–25 years using a likert scale-based questionnaire. The initial results are promising with all the questions having a mean score greater than 3.4. Volunteers also suggested the scope to improve user interface as well as the relevance of Q&As. [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.)

Git, a cross-platform and open source distributed version control tool, provides strong support for non-linear development and is capable of handling everything from small to large projects with speed and efficiency. It has become an indispensable tool for millions of software developers and is the de facto standard of version control in software development nowadays. However, despite its widespread use, developers still frequently face difficulties when using various Git commands to manage projects and collaborate. To better help developers use Git, it is necessary to understand the issues and difficulties that they may encounter when using Git. Unfortunately, this problem has not yet been comprehensively studied. To fill this knowledge gap, in this article, we conduct a large-scale study on Stack Overflow, a popular Q&A forum for developers.We extracted and analyzed 80,370 relevant questions from Stack Overflow, and reported the increasing popularity of the Git command questions. By analyzing the questions, we identified the Git commands that are frequently asked and those that are associated with difficult questions on Stack Overflow to help understand the difficulties developers may encounter when using Git commands. In addition, we conducted a survey to understand how developers learn Git commands in practice, showing that self-learning is the primary learning approach. These findings provide a range of actionable implications for researchers, educators, and developers. [ABSTRACT FROM AUTHOR]

JavaScript code blocks on Stack Overflow (SO) are often used in software projects. However, little is known about the issue of rule violation risk in SO JavaScript code blocks. Rule violation is one of the factors which degrades the quality of Java Script code. To prevent prevalence of rule violation by reusing SO JavaScript code blocks, it is needed to investigate how secure SO JavaScript code blocks are against rule violation. To examine the issue, we performed a quantitative analysis to investigate how many rule violations are, when first rule violation occurs and what is the trend of rule violations in evolution history of Stack Overflow JavaScript code blocks. We collected SO posts related to JavaScript and extracted the code blocks contained in the posts. By using ESLint, the most popular rule violation detection tool, we identified rule violations in the evolution history of our target code blocks. We then performed quantitative analyses on the identified rule violations. As the results of the analyses, we found that: (1) 60% of the studied code blocks evolve with any rule violations. (2) In the rule violated code blocks, 92% of the code blocks get first rule violation occurrence in the early phase of their evolution. (3) 80% of the rule violated code blocks never fix existing rule violations during their evolution. Our findings suggest that SO should provide a policy which can reduce rule violations in submitted JavaScript code blocks. The findings can also make SO users attend to rule violations when reusing SO JavaScript code blocks. [ABSTRACT FROM AUTHOR]

Developer forums are essential for software engineers to solve their problems with the assistance of experts on such forums. However, sometimes the solutions (answers) of a problem are not satisfactory or challenging to select the potential answer. Information seekers usually browse all the answers within the question thread to get the potential answer. The manual selection of correct answers is a tedious and time-consuming task. In this paper, we propose an automatic classification approach to predict the correct answers for developer forums. We first extract the metadata and combination of Q/A for each thread of the developer community (Stack Overflow). Then, the natural language processing techniques are applied to preprocess the Q/A combinations of the given dataset. After that, a keyword ranking algorithm is leveraged to extract keywords and their ranking scores for each Q/A combination. Based on keywords and their ranking scores for each Q/A combination, a keywords-based feature vector is constructed. Subsequently, word embedding is leveraged to convert each preprocessed Q/A combination into a text-based feature vector. Finally, we pass the metadata, keywords-based features, and text-based features to the ensemble deep learning model for training to predict correct answers. The results of 10-fold cross-validation specify that the proposed approach is accurate and surpasses the state-of-the-art. On average, it improves the accuracy, precision, recall, and f-measure up to 1.72%, 24.96%, 6.57%, and 16.62%, respectively.

Although the use of network simulator (NS) in predicting the behavior of computer networks has increased, the users often face a variety of challenges and share them on Stack Overflow (SO). However, the challenges that users deal with have not been studied. This paper presents an NS discussion dataset extracted from SOTorrent, which consists of 2,322 NS-related question posts spanning 17 features. The process of data collection was conducted in five steps, including filtering initial post dataset using simulator tags, discovering NS-related tags, collecting the tagged posts, extracting the posts title and preprocessing for LDA (Latent Dirichlet Allocation), and finally applying the LDA topic modeling to obtain the NS posts clustered into eight different topic names. We believe that this dataset will help research community in highlighting issues faced by NS users.

The emerging trend in technical research is to use client-generated data composed of community media to survey community opinion and scientific communication on employment and care issues. This study gathered data from the social website questions and answers and the indirect effects of stack to survey the key factors that influence public preferences in technical knowledge and opinions. Using a web search engine, topic modeling, and regression data modeling, this study quantitatively analyzed the effects of the textual and auxiliary functions of the response on the number of votes received with the response. Compared to previous surveys based on open assessments, the model results show that Quora users are more likely to talk only about technology. It may fail when query keywords do not match the text content of large documents containing relevant questions from the existing methods. Furthermore, consumers are generally not experts and provide ambiguous queries (Q&As) that lead to mixed results and face a problem with existing methods. To solve these problems, the researchers attempted to reorganize the primary results and present advanced distributed topic modeling techniques to address technologies and platforms by increasing the attributes and the time and space required to generate the model. This work briefly describes the public question-and-answer structure around the world and follows the development of the main themes of housing and employment opportunities for next-generation technologies worldwide in real-time scrolling. [ABSTRACT FROM AUTHOR]

On Stack Overflow, users reuse 11,926,354 external links to share the resources hosted outside the Stack Overflow website. The external links connect to the existing programming-related knowledge and extend the crowdsourced knowledge on Stack Overflow. Some of the external links, so-called as repeated external links, can be shared for multiple times. We observe that 82.5% of the link sharing activities (i.e., sharing links in any question, answer, or comment) on Stack Overflow share external resources, and 57.0% of the occurrences of the external links are sharing the repeated external links. However, it is still unclear what types of external resources are repeatedly shared. To help users manage their knowledge, we wish to investigate the characteristics of the repeated external links in knowledge sharing on Stack Overflow. In this paper, we analyze the repeated external links on Stack Overflow. We observe that external links that point to the text resources (hosted in documentation websites, tutorial websites, etc.) are repeatedly shared the most. We observe that different users repeatedly share the same knowledge in the form of repeated external links, thus increasing the maintenance effort of knowledge (e.g., update invalid links in multiple posts). The repeated external links can bring risks to the software engineering process, as 1) the same users can repeatedly share the external links for the purpose of promotion, and 2) external links can point to webpages with an overload of information that makes it difficult for users to retrieve relevant information. Our findings provide insights to Stack Overflow moderators and researchers. For example, we encourage Stack Overflow to centrally manage the commonly occurring knowledge in the form of repeated external links in order to better maintain the crowdsourced knowledge on Stack Overflow. [ABSTRACT FROM AUTHOR]

An essential part of software maintenance and evolution, refactoring is performed by developers, regardless of technology or domain, to improve the internal quality of the system, and reduce its technical debt. However, choosing the appropriate refactoring strategy is not always straightforward, resulting in developers seeking assistance. Although research in refactoring is well-established, with several studies altering between the detection of refactoring opportunities and the recommendation of appropriate code changes, little is known about their adoption in practice. Analyzing the perception of developers is critical to understand better what developers consider to be problematic in their code and how they handle it. Additionally, there is a need for bridging the gap between refactoring, as research, and its adoption in practice, by extracting common refactoring intents that are more suitable for what developers face in reality. In this study, we analyze refactoring discussions on Stack Overflow through a series of quantitative and qualitative experiments. Our results show that Stack Overflow is utilized by a diverse set of developers for refactoring assistance for a variety of technologies. Our observations show five areas that developers typically require help with refactoring– Code Optimization, Tools and IDEs, Architecture and Design Patterns, Unit Testing, and Database. We envision our findings better bridge the support between traditional (or academic) aspects of refactoring and their real-world applicability, including better tool support. [ABSTRACT FROM AUTHOR]

Design contexts are factors that shape a design, and whilst they are recognised by developers, they are typically tacit. Unlike software requirements, software engineering researchers have paid little attention to design contexts and there is little or no systematic research on how design contexts influence design. In this paper, we conduct an empirical investigation using Stack Overflow with the aim of mining design context knowledge that is related to design patterns. We chose to study design patterns because they are clear and identifiable. In this work, we develop a new taxonomy of design context terms related to design patterns. We introduce a new automated mining approach, DPC Miner, for mining design context knowledge from Stack Overflow. Finally, we analyse the Stack Overflow posts and present how design context impacts decisions about design patterns in practice. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution has considerably sped up the pace of skill changes in many professional domains, with scores of new skills emerging and many old skills moving towards obsolescence. For these domains, identifying the new necessary skills in a timely manner is a difficult task, where existing methods are inadequate. Understanding the process, by which these new skills and technologies appear in and diffuse through a professional domain, could give training providers more time to identify these new skills and react. For this purpose, in the present work, we look at the dynamics between online learning platforms and online hiring platforms in the software programming profession, a rapidly evolving domain. To do so, we fuse four data sources together: Stack Overflow, an online community questions and answers (Q&A) platform; Google Trends, which provides online search trends from Google; Udemy, a platform offering skill-based Massively Open Online Courses (MOOCs) where anyone can create courses; and Stack Overflow Jobs, a job ad platform. We place these platforms along two axes: i) how much expertise it takes, on average, to create content on them, and ii) whether, in general, the decision to create content on them is made by individuals or by groups. Our results show that the topics under study have a systematic tendency to appear earlier on platforms where content creation requires (on average) less expertise and is done more individually, rather than by groups: Stack Overflow is found to be more agile than Udemy, which is itself more agile than Stack Overflow Jobs (Google Trends did not prove usable due to extreme data sparsity). However, our results also show that this tendency is not present for all new skills, and that the software programming profession as a whole is remarkably agile: there are usually only a few months between the first Stack Overflow appearance of a new topic, and its first appearance on Udemy or Stack Overflow Jobs. In addition, we find that Udemy's agility has dramatically increased over time. Our novel methodology is able to provide valuable insights into the dynamics between online education and job ad platforms, enabling training program creators to look at said dynamics for various topics and to understand the pace of change. This allows them to maintain better awareness of the trends and to prioritize their attention, both on the right topics and on the right platforms. [ABSTRACT FROM AUTHOR]

Stack Overflow is a popular Community-based Question Answer (CQA) website focused on software programming and has attracted more and more users in recent years. However, duplicate questions frequently appear in Stack Overflow and they are manually marked by the users with high reputation. Automatic duplicate question detection alleviates labor and effort for users with high reputation. Although existing approaches extract textual features to automatically detect duplicate questions, these approaches are limited since semantic information could be lost. To tackle this problem, we explore the use of powerful deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), to detect duplicate questions in Stack Overflow. In addition, we use Word2Vec to obtain the vector representations of words. They can fully capture semantic information at document-level and word-level respectively. Therefore, we construct three deep learning approaches WV-CNN, WV-RNN and WV-LSTM, which are based on Word2Vec, CNN, RNN and LSTM, to detect duplicate questions in Stack Overflow. Evaluation results show that WV-CNN and WV-LSTM have made significant improvements over four baseline approaches (i.e., DupPredictor, Dupe, DupPredictorRep-T, and DupeRep) and three deep learning approaches (i.e., DQ-CNN, DQ-RNN, and DQ-LSTM) in terms of recall-rate@5, recall-rate@10 and recall-rate@20. Furthermore, the experimental results indicate that our approaches WV-CNN, WV-RNN, and WV-LSTM outperform four machine learning approaches based on Support Vector Machine, Logic Regression, Random Forest and eXtreme Gradient Boosting in terms of recall-rate@5, recall-rate@10 and recall-rate@20.

Deep learning has developed rapidly in recent years, attracting the attention of numerous researchers. Since a wide range of topics are covered in this field, we are wondering what topics researchers have concerned about. However, after investigation, we find that very few researchers have paid attention to this demand. In this paper, we conduct a large-scale study to analyze the questions faced by deep learning developers. We use Stack Overflow, one of the largest question&answer sites, as our data source, and extract 32969 posts about deep learning as our studied dataset. After filtering, augmenting and pre-processing the post datasets from Stack Overflow, we use the latent Dirichlet allocation (LDA) topic model to summarize 30 topics based on their text content. In addition, we measure the difficulty and popularity of each topic, compare the different issues faced by different deep learning frameworks, and analyze the development trend of each topic. Our main results are as follows: (1) developers ask a broad spectrum of questions about deep-learning, ranging from Data Shape to Object Detection; (2) Gradient Propagation is the most popular among all the topics and (3) Object Detection is the most difficult; (4) issues of Package Installation, Code Understanding and Method Introduction are common in the current different deep learning frameworks; (5) there are three trends in these topics, e.g., a significant rising trend is found in the number of discussion on Data Shape. Finally, based on our research findings, we make some targeted and valuable suggestions for developers, researchers, educators, and framework providers of deep learning. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the Internet via smart computing devices. It is a rapidly emerging paradigm that encompasses almost every aspect of our modern life, such as smart home, cars, and so on. With interest in IoT growing, we observe that the IoT discussions are becoming prevalent in online developer forums, such as Stack Overflow (SO). An understanding of such discussions can offer insights into the prevalence, popularity, and difficulty of various IoT topics. For this paper, we download a large number of SO posts that contain discussions about various IoT technologies. We apply topic modeling on the textual contents of the posts. We label the topics and categorize the topics into hierarchies. We analyze the popularity and difficulty of the topics. Our study offers several findings. First, IoT developers discuss a range of topics in SO related to Hardware, Software, Network, and Tutorials. Second, secure messaging using IoT devices from the Network category is the most prevalent topic, followed by scheduling of IoT script in the Software category. Third, all the topic categories are evolving rapidly in SO, i.e., new questions are being added more and more in SO about IoT tools and techniques. Fourth, the “How” type of questions are asked more across the three topic categories (Software, Network, and Hardware), although a large number of questions are also of the “What” type: IoT developers are using SO not only to discuss how to address a problem related to IoT, but also to learn what the different IoT techniques and tools offer. Fifth, topics related to data parsing and micro-controller configuration are the most popular. Sixth, topics related to multimedia streaming and Bluetooth are the most difficult. Our study findings have implications for all four different IoT stakeholders: tool builders, developers, educators, and researchers. For example, IoT developers and newcomers can use our findings on topic popularity to learn about popular IoT techniques. Educators and researchers can make more tutorials or develop new techniques to make difficult IoT topics easier. IoT tool builders can look at our identified topics and categories to learn about IoT developers’ preferences, which then can help them develop new tools or enhance their current offerings. [ABSTRACT FROM AUTHOR]

Many Q&A sites initially run only in English, and then gradually release their multi-lingual variants to serve users who speak other languages. The launch of such multi-lingual sites always lead to an intense dispute about the pros and cons of multi-lingual sites. Although all arguments and concerns sound reasonable, people can rarely provide solid evidence to convince each other. In this paper, from users' comments about the launch of several non-English Stack Overflow sites, we first identify three major concerns including community split, knowledge needs and interests in other languages, and knowledge fragmentation and duplication. To validate these three concerns, we conduct an evidence-based data analysis and comparison of user characteristics, tag usage and cross-site links between the Russian Stack Overflow and the English Stack Overflow on these three concerns. Our study sheds light on the existence value and risks of multi-lingual Q&A sites. [ABSTRACT FROM AUTHOR]

In the digital economy, innovation processes increasingly rely on highly specialised know-how and open-source software shared on digital platforms on collaborative programming. The information that feeds into the content on these platforms is provided voluntarily by a vast crowd of knowledgeable users from all over the world. In contributing to the platforms, users invest their time and share knowledge with strangers to add to the rising body of digital knowledge. This requires an open mindset and trust. In this study, we argue that such a mindset is not just an individual asset but determined by the local communities the users are embedded in. We, therefore, hypothesise that places with higher levels of trust should contribute more to Stack Overflow, the world's largest question-and-answer platform for programming questions. In relating the city-level contributions of 266 OECD metropolitan areas to infrastructure, economic, and trust measures, we find support for this hypothesis. In contrast, click rates to the platform are solely driven by infrastructure and economic variables, but not by trust. These findings highlight the importance of societal values in the twenty-first-century knowledge economy: if policymakers want to develop a lively local digital economy, it is not enough to provide fast Internet access and business opportunities. Instead, it is equally important to establish a trust-building environment that fosters sharing of innovative ideas, collaborations, and knowledge spillovers. Code and Data: [ABSTRACT FROM AUTHOR]

In the field of software engineering, practitioners' share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors' mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves 6 3. 8 % accuracy in binary classification in SO design patterns tag and 7 1. 6 % accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system's predicted reputation labels match the quality of provided answers. [ABSTRACT FROM AUTHOR]

GitHub and Stack Overflow are often used together for software development. GH-SO users, who use both GitHub and Stack Overflow, contribute to the development of various software projects in GitHub and share their knowledge and experience on software development in Stack Overflow. To widely understand the interests and working habits of GH-SO users on software development, it is important to investigate how GH-SO users utilize GitHub and Stack Overflow. In this paper, we present an exploratory study on GitHub commit and Stack Overflow post activities of GH-SO users. Specifically, we investigate the working habits of GH-SO users on GitHub commit and Stack Overflow post activities. We randomly selected 19,756 of GH-SO users as our target sample and collected 2,819,483 and 2,147,317 of commit activity data and post activity data of the GH-SO users. We then categorized the collected commit and post activity datasets into specific categories on programming languages and statistically analyzed the categorized commit and post activity datasets. As the results of our analysis, we found the following: (1) The overall commit and post activities of the GH-SO users share some similarity. (2) The commit activities gradually change while the post activities drastically change over time. (3) The commit activities of the GH-SO users are broadly distributed while the post activities are narrowly distributed and the commit activity can be better predictor for post activity. (4) The commit activity of the GH-SO users tends to be performed prior post activity. We believe that our findings can contribute to finding the ways to better support commit and post activities of GitHub and Stack Overflow users. [ABSTRACT FROM AUTHOR]

Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions. The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning‐based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K‐Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni‐gram feature set provides best classification results and attains 85.80% accuracy. For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni‐gram feature set proved to be the most suitable. [ABSTRACT FROM AUTHOR]

Logging is an integral part of software development. Software practitioners often face issues in software logging, and they post these issues on Q&A websites to take suggestions from the experts. In this study, we perform a three‐level empirical analysis of logging questions posted on six popular technical Q&A websites, namely, Stack Overflow (SO), Serverfault (SF), Superuser (SU), Database Administrators (DB), Software Engineering (SE), and Android Enthusiasts (AE). The findings show that logging issues are prevalent across various domains, for example, database, networks, and mobile computing, and software practitioners from different domains face different logging issues. The semantic analysis of logging questions using Latent Dirichlet Allocation (LDA) reveals trends of several existing and new logging topics, such as logging conversion pattern, Android device logging, and database logging. In addition, we observe specific logging topics for each website: DB (log shipping and log file growing/shrinking), SU (event log and syslog configuration), SF (log analysis and syslog configuration), AE (app install and usage tracking), SE (client server logging and exception logging), and SO (log file creation/deletion, Android emulator logging, and logger class of Log4j). We obtain an increasing trend of logging topics on the SO, SU, and DB websites whereas a decreasing trend of logging topics on the SF website. Logging accelerates bug fixes in software engineering and is thus used by several stakeholders, including database, system, and network administrators. To this end, we study the logging requirements by analyzing the concerns of various stakeholders from six Q/A websites of Stack Exchange Network. The results show an increasing trend of logging among system and database administrators whereas a decreasing trend among server administrators. Furthermore, the Android Enthusiasts website has the highest unsuccessful question ratio, depicting dissatisfaction among the community. [ABSTRACT FROM AUTHOR]

Code maintenance data sets typically consist of a before version of the code and an after version that contains the improvement or fix. Such data sets are important for various software engineering support tools related to code maintenance, such as program repair, code recommender systems, or Application Programming Interface (API) misuse detection. Most of the current data sets are typically constructed from mining commit history in version-control systems or issues in issue-tracking systems. In this paper, we investigate whether Stack Overflow can be used as an additional source for building code maintenance data sets. Comments on Stack Overflow provide an effective way for developers to point out problems with existing answers, alternative solutions, or pitfalls. Given its crowd-sourced nature, answers are then updated to incorporate these suggestions. In this paper, we mine comment-edit pairs from Stack Overflow and investigate their potential usefulness for constructing the above data sets. These comment-edit pairs have the added benefit of having concrete descriptions/explanations of why the change is needed as well as potentially having less tangled changes to deal with. We first design a technique to extract related comment-edit pairs and then qualitatively and quantitatively investigate the nature of these pairs. We find that the majority of comment-edit pairs are not tangled, but find that only 27% of the studied pairs are potentially useful for the above applications. We categorize the types of mined pairs and find that the highest ratio of useful pairs come from those categorized as Correction, Obsolete, Flaw, and Extension. These categories can provide data for both corrective and preventative maintenance activities. To demonstrate the effectiveness of our extracted pairs, we submitted 15 pull requests to popular GitHub repositories, 10 of which have been accepted to widely used repositories such as Apache Beam () and NLTK (). Our work is the first to investigate Stack Overflow comment-edit pairs and opens the door for future work in this direction. Based on our findings and observations, we provide concrete suggestions on how to potentially identify a larger set of useful comment-edit pairs, which can also be facilitated by our shared data. [ABSTRACT FROM AUTHOR]

API tutorials are important learning resources as they explain how to use certain APIs in a given programming context. An API tutorial can be split into a number of units. Consecutive units that describe a same topic are often called a tutorial fragment. We consider the API explained by a tutorial fragment as an API tag. Generating API tags for a tutorial fragment can help understand, navigate, and retrieve the fragment. Existing approaches often do not perform well on API tag generation due to high manual effort and low accuracy. Like API tutorials, Stack Overflow (SO) is also an important learning resource that provides the explanations of APIs. Thus, SO posts also contain API tags. Besides, API tags of SO posts are abundant and can be extracted easily. In this paper, we propose a novel approach ATTACK (stands for A PI T ag for T utorial frA gments using C rowd K nowledge), which can automatically generate API tags for tutorial fragments from SO posts. ATTACK first constructs Q & A p a i r , t a g s e t pairs by extracting API tags of SO posts. Then, it trains a deep neural network with the attention mechanism to learn the semantic relatedness between Q&A pairs and the associated API tags, taking into consideration both textual descriptions and code in a Q&A pair. Finally, the trained model is used to generate API tags for tutorial fragments. We evaluate ATTACK on public Java and Android datasets containing 43,132 Q & A p a i r , t a g s e t pairs. Experimental results show that ATTACK is effective and outperforms the state-of-the-art approaches in terms of F-Measure. Our user study further confirms the effectiveness of ATTACK in generating API tags for tutorial fragments. We also apply ATTACK to document linking and the results confirm the usefulness of API tags generated by ATTACK. [ABSTRACT FROM AUTHOR]

Context: Mobile application developers are getting more concerned due to the importance of quality requirements or non-functional requirements (NFR) in software quality. Developers around the globe are actively asking a question(s) and sharing solutions to the problems related to software development on Stack Overflow (SO). The knowledge shared by developers on SO contains useful information related to software development such as feature requests (functional/non-functional), code snippets, reporting bugs or sentiments. Extracting the NFRs shared by iOS developers on programming Q&A website SO has become a challenge and a less researched area. Objective: To identify and understand the real problems, needs, trends, and the critical NFRs or quality requirements discussed on Stack Overflow related to iOS mobile application development. Method: We extracted and used only the iOS posts data of SO. We applied the well-known statistical topical model Latent Dirichlet Allocation (LDA) to identify the main topics in iOS posts on SO. Then, we labeled the extracted topics with quality requirements or NFRs by using the wordlists to assess the trend, evolution, hot and unresolved NFRs in all iOS discussions. Results: Our findings revealed that the highly frequent topics the iOS developers discussed are related to usability, reliability, and functionality followed by efficiency. Interestingly, the most problematic areas unresolved are also usability, reliability, and functionality though followed by portability. Besides, the evolution trend of each of the six different quality requirements or NFRs over time is depicted through comprehensive visualization. Conclusion: Our first empirical investigation on approximately 1.5 million iOS posts and comments of SO gives insight on comprehending the NFRs in iOS application development through the lens of real-world practitioners.