Automatic prediction of rejected edits in Stack Overflow

The content quality of shared knowledge in Stack Overflow (SO) is critical in supporting software developers with their programming problems. Thus, it allows its users to suggest editing the software to improve the quality of a post. However, existing all research shows that many suggested edits in SO are rejected due to undesired contents or violating editing guidelines. Such a scenario frustrates or demotivates users who would like to conduct good-quality edits. we propose Semantically Tag and Score Recommendation, with the use of the deep learning-based approach that automatically recommends tags or grades or scores through learning the semantics of both tags, score, grade and questions in such software CQA. First, word embedding is employed to convert text information to high-dimension vectors for better representing questions and tags. Second, a Multitasking, the core modules of Semantically Tag and Score Recommendation, is designed to capture short and long semantics. Third, the learned semantic vectors are fed into a gradient descent-based algorithm for classification

It is poorly understood how developers discover and adopt software development innovations such as tools, libraries, frameworks, or web sites that support developers. Yet, being aware of and choosing appropriate tools and components can have a significant impact on the outcome of a software project. In our study, we investigate link sharing on Stack Overflow to gain insights into how software developers discover and disseminate innovations. We find that link sharing is a significant phenomenon on Stack Overflow, that Stack Overflow is an important resource for software development innovation dissemination and that its part of a larger interconnected network of online resources used and referenced by developers. This knowledge can guide researchers and practitioners who build tools and services that support software developers in the exploration, discovery, and adoption of software development innovations.

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the internet/network via smart computing devices. IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, it is crucial to ensure IoT devices follow strict security requirements. At the same time, the prevalence of IoT devices offers developers a chance to design and develop Machine Learning (ML)-based intelligent software systems using their IoT devices. However, given the diversity of IoT devices, IoT developers may find it challenging to introduce appropriate security and ML techniques into their devices. Traditionally, we learn about the IoT ecosystem/problems by conducting surveys of IoT developers/practitioners. Another way to learn is by analyzing IoT developer discussions in popular online developer forums like Stack Overflow (SO). However, we are aware of no such studies that focused on IoT developers’ security and ML-related discussions in SO. This paper offers the results of preliminary study of IoT developer discussions in SO. First, we collect around 53K IoT posts (questions + accepted answers) from SO. Second, we tokenize each post into sentences. Third, we automatically identify sentences containing security and ML-related discussions. We find around 12% of sentences contain security discussions, while around 0.12% sentences contain ML-related discussions. There is no overlap between security and ML-related discussions, i.e., IoT developers discussing security requirements did not discuss ML requirements and vice versa. We find that IoT developers discussing security issues frequently inquired about how the shared data can be stored, shared, and transferred securely across IoT devices and users. We also find that IoT developers are interested to adopt deep neural network-based ML models into their IoT devices, but they find it challenging to accommodate those into their resource-constrained IoT devices. Our findings offer implications for IoT vendors and researchers to develop and design novel techniques for improved security and ML adoption into IoT devices.

A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable impact on the software development process. However, if developers do not get their expected answers, the websites will not be useful, and software development time will increase. Stack Overflow is the most popular CQA concerning programming problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is the user's acceptance. In this paper, we investigate unresolved questions, without accepted answers, on Stack Overflow. The number of unresolved questions is increasing. As of August 2019, 47% of Stack Overflow questions were unresolved. In this study, we analyze the effectiveness of various features, including some novel features, to resolve a question. We do not use the features that contain information not present at the time of asking a question, such as answers. To evaluate our features, we deploy several predictive models trained on the features of 18 million questions to predict whether a question will get an accepted answer or not. The results of this study show a significant relationship between our proposed features and getting accepted answers. Finally, we introduce an online tool that predicts whether a question will get an accepted answer or not. Currently, Stack Overflow's users do not receive any feedback on their questions before asking them, so they could carelessly ask unclear, unreadable, or inappropriately tagged questions. By using this tool, they can modify their questions and tags to check the different results of the tool and deliberately improve their questions to get accepted answers.

In recent years, programming-related Q &A forums like Stack Overflow are rapidly gaining popularity. For many developers, the cumulative expertise of programmers and developers is a valuable resource, but it can raise worries about the quality of shared content. To maintain quality the moderators close low-quality and irrelevant questions. As only a few users who are moderators can close questions, it is a difficult task for them. In this paper, we offer a strategy for forecasting the future state of the questions on Stack Overflow by focusing on the post’s feature extraction. We classified a question into one of the five classes according to the newly updated reasons: (1) open, (2) off-topic, (3) unclear, (4) too-broad, and (5) opinion-based. We collected our dataset from the Stack Overflow data dump and the old dataset from Kaggle. We experimented with several machine learning models and achieved state-of-the-art results using XGBoost. Our method can be used to filter questions at the posting time, reducing the moderators’ overhead. To the best of our knowledge, after the update on closing reasons, our study is the first on closed question prediction.

Stack Overflow is a Q&A website that is popular among developers and extensively used in software engineering (SE) research. A significant body of research has examined how Stack Overflow can assist with software development tasks, such as recommending APIs. However, while researchers have recognized the importance of Stack Overflow in SE research related to software development tasks, the specific ways in which it is utilized and the reasons for its widespread usage in research have not been thoroughly explored. To address these knowledge gaps, we conducted the first study to understand the role of Stack Overflow in assisting with SE research regarding software development tasks by systematically examining relevant and high-quality research works. Meanwhile, we carried out a qualitative survey to gain insight into why researchers choose to utilize Stack Overflow in SE research and to solicit suggestions for the better use of Stack Overflow in research. The study identifies trends in the research area, prominent researchers and organizations, and the types of tasks that utilize Stack Overflow in research, with coding and debugging being the most common. Moreover, it examines how Stack Overflow data is utilized in SE research regarding software development tasks, including searching, training models, and mining associations. Our qualitative survey of researchers indicates that the popularity of Stack Overflow stems from its comprehensive explanations of technical topics that are often not found in documentation or manuals. The findings provide a comprehensive understanding of the role of Stack Overflow in SE research regarding software development tasks, and offer actionable implications for both researchers and stakeholders of Stack Overflow to facilitate future research and improvements.

Question-and-answer platforms such as Stack Overflow are an important way for software developers to share and retrieve knowledge. However, reusing poorly understood code can lead to serious problems, such as bugs or security vulnerabilities. To better understand how code comments affect the perceived helpfulness of Stack Overflow answers, we conducted an online experiment simulating a Stack Overflow environment (n=91). The results indicate that both block and inline comments are perceived as significantly more helpful than uncommented source code. Moreover, novices rated code snippets with block comments as more helpful than those with inline comments. Interestingly, other surface features, such as the position of an answer and its answer score, were considered less important. Moreover, the content of Stack Overflow has been a major source for training large language models. AI-based coding assistants such as GitHub Copilot, which are based on these models, are changing the way Stack Overflow is used. However, our findings have implications beyond Stack Overflow. First, they may help to improve the relevance also of other community-driven platforms, which provide human advice and explanations of code solutions, complementing AI-based support for software developers. Second, since chat-based AI tools can be prompted to generate code in different ways, knowing which properties influence perceived helpfulness can lead to more targeted prompting strategies to generate readable code snippets.

On Stack Overflow, developers can not only browse question posts to solve their programming problems but also gain expertise from the question posts to help improve their programming skills. Therefore, improving the quality of question posts in Stack Overflow has attracted the wide attention of researchers. A concise and precise title can play an important role in helping developers understand the key information of the question post, which can improve the post quality. However, the quality of the generated title is not high due to the lack of professional knowledge related to their questions or the poor presentation ability of developers. A previous study aimed to automatically generate the title by analyzing the code snippets in the question post. However, this study ignored the useful information in the corresponding problem description. Therefore, we propose an approach SOTitle for automatic post title generation by leveraging the code snippets and the problem description in the question post (i.e., the multi-modal input). SOTitle follows the Transformer structure, which can effectively capture long-term dependencies through a multi-head attention mechanism. To verify the effectiveness of SOTitle, we construct a large-scale high-quality corpus from Stack Overflow, which includes 1,168,257 high-quality question posts for four popular programming languages. Experimental results show that SOTitle can significantly outperform six state-of-the-art baselines in both automatic evaluation and human evaluation. To encourage follow-up studies, we make our corpus and approach publicly available

Stack Overflow is a popular question answering site that is focused on programming problems. Despite efforts to prevent asking questions that have already been answered, the site contains duplicate questions. This may cause developers to unnecessarily wait for a question to be answered when it has already been asked and answered. The site currently depends on its moderators and users with high reputation to manually mark those questions as duplicates, which not only results in delayed responses but also requires additional efforts. In this paper, we first perform a manual investigation to understand why users submit duplicate questions in Stack Overflow. Based on our manual investigation we propose a classification technique that uses a number of carefully chosen features to identify duplicate questions. Evaluation using a large number of questions shows that our technique can detect duplicate questions with reasonable accuracy. We also compare our technique with DupPredictor, a state-of-the-art technique for detecting duplicate questions, and we found that our proposed technique has a better recall-rate than that technique.

The success of technical Q&A sites such as Stack Overflow depends on two key factors: (a) active user participation and (b) the quality of the shared knowledge. Stack Overflow introduced an edit system that allows users to suggest improvements to posts (i.e., questions and answers) to enhance the quality of the content. However, users, such as post owners or site moderators, can reject these suggested edits by rollbacks due to unsatisfactory, low-quality edits or violating edit guidelines. Unfortunately, subjectivity bias in determining whether an edit is satisfactory or unsatisfactory can lead to inconsistencies in the rollback decisions. For example, one user might accept the formatting of a method name (e.g., getActivity()) as a code term, while another might reject it. Such inconsistencies can demotivate and frustrate users whose edits are rejected. Furthermore, several post owners prefer to keep their content unchanged and even resist necessary edits. As a result, they sometimes roll back necessary edits and revert posts to a flawed version, which violates editing guidelines. The problems mentioned above are further compounded by the lack of specific guidelines and tools to assist users in ensuring consistency in user rollback actions. In this study, we investigate the types, prevalence, and impact of rollback edit inconsistencies and propose a solution to address them. The outcomes of this research are fivefold. First, we manually investigated 764 rollback edits (382 questions + 382 answers) and identified eight types of inconsistent rollback. Second, we surveyed 44 practitioners to assess the impact of rollback inconsistencies. More than 80% of the participants found our identified inconsistency types detrimental to post quality. Third, we developed rule-based algorithms and Machine Learning (ML) models to detect the eight types of rollback inconsistencies. Both approaches achieve over 90% accuracy. Fourth, we introduced a tool, iEdit, which integrates these algorithms into a browser extension and assists Stack Overflow users during their edits. Fifth, we surveyed 16 Stack Overflow users to evaluate the effectiveness of iEdit. The participants found the tool’s suggestions helpful in avoiding inconsistent rollback edits.

Mining API usage scenarios from stack overflow

The development of straightforward classification methods is needed to identify unstable aneurysms and rupture risk for clinical use. In this study, we aim to investigate the relative importance of geometrical, hemodynamic and clinical risk factors represented by the PHASES score for predicting aneurysm wall enhancement using several machine-learning (ML) models. Nine different ML models were applied to 65 aneurysm cases with 24 predictor variables. ML models were optimized with the training set using tenfold cross-validation with five repeats with the area under the curve (AUC) as cost parameter. Models were validated using the test set. Accuracy being significantly higher (p < 0.05) than the non-information rate (NIR) was used as measure of performance. The relative importance of the predictor variables was determined from a subset of five ML models in which this information was available. Best-performing ML model was based on gradient boosting (AUC = 0.98). Second best-performing model was based on generalized linear modeling (AUC = 0.80). The size ratio was determined as the dominant predictor for wall enhancement followed by the PHASES score and mean wall shear stress value at the aneurysm wall. Four ML models exhibited a statistically significant higher accuracy (0.79) than the NIR (0.58): random forests, generalized linear modeling, gradient boosting and linear discriminant analysis. ML models are capable of predicting the relative importance of geometrical, hemodynamic and clinical parameters for aneurysm wall enhancement. Size ratio, PHASES score and mean wall shear stress value at the aneurysm wall are of highest importance when predicting wall enhancement in cerebral aneurysms.

PostFinder: Mining Stack Overflow posts to support software developers

Traditional web search forces the developers to leave their working\nenvironments and look for solutions in the web browsers. It often does not\nconsider the context of their programming problems. The context-switching\nbetween the web browser and the working environment is time-consuming and\ndistracting, and the keyword-based traditional search often does not help much\nin problem solving. In this paper, we propose an Eclipse IDE-based web search\nsolution that collects the data from three web search APIs-- Google, Yahoo,\nBing and a programming Q &amp; A site-- Stack Overflow. It then provides search\nresults within IDE taking not only the content of the selected error into\naccount but also the problem context, popularity and search engine\nrecommendation of the result links. Experiments with 25 run time errors and\nexceptions show that the proposed approach outperforms the keyword-based search\napproaches with a recommendation accuracy of 96%. We also validate the results\nwith a user study involving five prospective participants where we get a result\nagreement of 64.28%. While the preliminary results are promising, the approach\nneeds to be further validated with more errors and exceptions followed by a\nuser study with more participants to establish itself as a complete IDE-based\nweb search solution.\n

Stack Overflow is one of the largest and most popular question-answering (Q&A) websites. It accumulates millions of programming related questions and answers to support the developers in software development. Unfortunately, a large number of questions are not answered at all, which might hurt the quality or purpose of this community-oriented knowledge base. Up to 29% of Stack Overflow questions do not have any answers. There have been existing attempts in detecting the unanswered questions. Unfortunately, they primarily rely on the question attributes (e.g., score, view count) that are not available during the submission of a question. Detection of the potentially unanswered questions in advance during question submission could help one improve the question and thus receive the answers in time. In this paper, we compare unanswered and answered questions quantitatively and qualitatively by analyzing a total of 4.8 million questions from Stack Overflow. We find that topics discussed in the question, the experience of the question submitter, and readability of question texts could often determine whether a question would be answered or not. Our qualitative study also reveals several other non-trivial factors that not only explain (partially) why the questions remain unanswered but also guide the novice users to improve their questions. We develop four machine learning models to predict the unanswered questions during their submission. According to the experiments, our models predict the unanswered questions with a maximum of about 79% accuracy and significantly outperform the state-of-the-art prediction models.