ترغب بنشر مسار تعليمي؟ اضغط هنا

The Impacts of Sentiments and Tones in Community-Generated Issue Discussions

63   0   0.0 ( 0 )
 نشر من قبل Jinghui Cheng
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

The diverse community members who contribute to the discussions on issue tracking systems of open-source software projects often exhibit complex affective states such as sentiments and tones. These affective states can significantly influence the effectiveness of the issue discussions in elaborating the initial ideas into actionable tasks that the development teams need to address. In this paper, we present an extended empirical study to investigate the impacts of sentiments and tones in community-generated issue discussions. We created and validated a large dataset of sentiments and tones in the issues posts and comments created by diverse community members in three popular open source projects. Our analysis results drew a complex picture of the relationships between, on the one hand, the sentiments and tones in the issue discussions, and on the other hand, various discussion and development-related measures such as the discussion length and the issue resolution time. We also found that when factors such as the issue poster roles and the issue types were controlled, sentiments and tones had varied associations with the measures. Insights gained from these findings can support open source community members in making and moderating effective issue discussions and guide the design of tools to better support community engagement.



قيم البحث

اقرأ أيضاً

261 - Lin Shi , Ziyou Jiang , Ye Yang 2021
Collaborative live chats are gaining popularity as a development communication tool. In community live chatting, developers are likely to post issues they encountered (e.g., setup issues and compile issues), and other developers respond with possible solutions. Therefore, community live chats contain rich sets of information for reported issues and their corresponding solutions, which can be quite useful for knowledge sharing and future reuse if extracted and restored in time. However, it remains challenging to accurately mine such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the problem of issue-solution pair extraction from developer live chat data, and propose an automated approach, named ISPY, based on natural language processing and deep learning techniques with customized enhancements, to address the problem. Specifically, ISPY automates three tasks: 1) Disentangle live chat logs, employing a feedforward neural network to disentangle a conversation history into separate dialogs automatically; 2) Detect dialogs discussing issues, using a novel convolutional neural network (CNN), which consists of a BERT-based utterance embedding layer, a context-aware dialog embedding layer, and an output layer; 3) Extract appropriate utterances and combine them as corresponding solutions, based on the same CNN structure but with different feeding inputs. To evaluate ISPY, we compare it with six baselines, utilizing a dataset with 750 dialogs including 171 issue-solution pairs and evaluate ISPY from eight open source communities. The results show that, for issue-detection, our approach achieves the F1 of 76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63% for solution-extraction and outperforms the baselines by 20%.
Lack of awareness and knowledge of microservices-specific security challenges and solutions often leads to ill-informed security decisions in microservices system development. We claim that identifying and leveraging security discussions scattered in existing microservices systems can partially close this gap. We define security discussion as a paragraph from developer discussions that includes design decisions, challenges, or solutions relating to security. We first surveyed 67 practitioners and found that securing microservices systems is a unique challenge and that having access to security discussions is useful for making security decisions. The survey also confirms the usefulness of potential tools that can automatically identify such security discussions. We developed fifteen machine/deep learning models to automatically identify security discussions. We applied these models on a manually constructed dataset consisting of 4,813 security discussions and 12,464 non-security discussions. We found that all the models can effectively identify security discussions: an average precision of 84.86%, recall of 72.80%, F1-score of 77.89%, AUC of 83.75% and G-mean 82.77%. DeepM1, a deep learning model, performs the best, achieving above 84% in all metrics and significantly outperforms three baselines. Finally, the practitioners feedback collected from a validation survey reveals that security discussions identified by DeepM1 have promising applications in practice.
Discussions is a new feature of GitHub for asking questions or discussing topics outside of specific Issues or Pull Requests. Before being available to all projects in December 2020, it had been tested on selected open source software projects. To un derstand how developers use this novel feature, how they perceive it, and how it impacts the development processes, we conducted a mixed-methods study based on early adopters of GitHub discussions from January until July 2020. We found that: (1) errors, unexpected behavior, and code reviews are prevalent discussion categories; (2) there is a positive relationship between project member involvement and discussion frequency; (3) developers consider GitHub Discussions useful but face the problem of topic duplication between Discussions and Issues; (4) Discussions play a crucial role in advancing the development of projects; and (5) positive sentiment in Discussions is more frequent than in Stack Overflow posts. Our findings are a first step towards data-informed guidance for using GitHub Discussions, opening up avenues for future work on this novel communication channel.
Modern open source software development heavily relies on the issue tracking systems to manage their feature requests, bug reports, tasks, and other similar artifacts. Together, those issues form a complex network with links to each other. The hetero geneous character of issues inherently results in varied link types and therefore poses a great challenge for users to create and maintain the label of the link manually. The goal of most existing automated issue link construction techniques ceases with only examining the existence of links between issues. In this work, we focus on the next important question of whether we can assess the type of issue link automatically through a data-driven method. We analyze the links between issues and their labels used the issue tracking system for 66 open source projects. Using three projects, we demonstrate promising results when using supervised machine learning classification for the task of link label recovery with careful model selection and tuning, achieving F1 scores of between 0.56-0.70 for the three studied projects. Further, the performance of our method for future link label prediction is convincing when there is sufficient historical data. Our work signifies the first step in systematically manage and maintain issue links faced in practice.
Developer discussions range from in-person hallway chats to comment chains on bug reports. Being able to identify discussions that touch on software design would be helpful in documentation and refactoring software. Design mining is the application o f machine learning techniques to correctly label a given discussion artifact, such as a pull request, as pertaining (or not) to design. In this paper we demonstrate a simple example of how design mining works. We then show how conclusion stability is poor on different artifact types and different projects. We show two techniques -- augmentation and context specificity -- that greatly improve the conclusion stability and cross-project relevance of design mining. Our new approach achieves AUC of 0.88 on within dataset classification and 0.80 on the cross-dataset classification task.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا