NLP2Code: Code Snippet Content Assist via Natural Language Tasks

Developers increasingly take to the Internet for code snippets to integrate into their programs. To save developers the time required to switch from their development environments to a web browser in the quest for a suitable code snippet, we introduce NLP2Code, a content assist for code snippets. Unlike related tools, NLP2Code integrates directly into the source code editor and provides developers with a content assist feature to close the vocabulary gap between developers' needs and code snippet meta data. Our preliminary evaluation of NLP2Code shows that the majority of invocations lead to code snippets rated as helpful by users and that the tool is able to support a wide range of tasks.Comment: tool demo video available at https://www.youtube.com/watch?v=h-gaVYtCznI; to appear as a tool demo paper at ICSME 2017 (https://icsme2017.github.io/

Asking Questions is Easy, Asking Great Questions is Hard: Constructing Effective Stack Overflow Questions

This paper explores and seeks to improve the ways in which Stack Overflow question posts can elicit answers. Using statistical data analysis approaches and reviews of existing literature, we pin- point three key factors that are found in many previously success- ful/answerable questions. We then present a prototypical sidebar for the ask page that leverages these factors to dynamically (1) evaluate the quality of questions in construction (2) display answer previews of relevant questions and (3) scaffold the identified factors to subsequent askers during their question development processes

Learning with comments: An analysis of comments and community on Stack Overflow

Stack Overflow (SO) has become a primary source for learning how to code, with community features supporting asking and answering questions, upvoting to signify approval of content, and comments to extend questions and answers. While past research has considered the value of posts, often based on upvoting, little has examined the role of comments. Beyond value in explaining code, comments may offer new ways of looking at problems, clarifications of questions or answers, and socially supportive community interactions. To understand the role of comments, a content analysis was conducted to evaluate the key purposes of comments. A coding schema of nine comment categories was developed from open coding on a set of 40 posts and used to classify comments in a larger dataset of 2323 comments from 50 threads over a 6-month period. Results provide insight into the way the comments support learning, knowledge development, and the SO community, and the use and usefulness of the comment feature

Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform

Many platforms collect crowdsourced information primarily from volunteers. As this type of knowledge curation has become widespread, contribution formats vary substantially and are driven by diverse processes across differing platforms. Thus, models for one platform are not necessarily applicable to others. Here, we study the temporal dynamics of Genius, a platform primarily designed for user-contributed annotations of song lyrics. A unique aspect of Genius is that the annotations are extremely local -- an annotated lyric may just be a few lines of a song -- but also highly related, e.g., by song, album, artist, or genre. We analyze several dynamical processes associated with lyric annotations and their edits, which differ substantially from models for other platforms. For example, expertise on song annotations follows a ``U shape'' where experts are both early and late contributors with non-experts contributing intermediately; we develop a user utility model that captures such behavior. We also find several contribution traits appearing early in a user's lifespan of contributions that distinguish (eventual) experts from non-experts. Combining our findings, we develop a model for early prediction of user expertise.Comment: 9 pages. 10 figure

Understanding the Role of Images on Stack Overflow

Images are increasingly being shared by software developers in diverse channels including question-and-answer forums like Stack Overflow. Although prior work has pointed out that these images are meaningful and provide complementary information compared to their associated text, how images are used to support questions is empirically unknown. To address this knowledge gap, in this paper we specifically conduct an empirical study to investigate (I) the characteristics of images, (II) the extent to which images are used in different question types, and (III) the role of images on receiving answers. Our results first show that user interface is the most common image content and undesired output is the most frequent purpose for sharing images. Moreover, these images essentially facilitate the understanding of 68% of sampled questions. Second, we find that discrepancy questions are more relatively frequent compared to those without images, but there are no significant differences observed in description length in all types of questions. Third, the quantitative results statistically validate that questions with images are more likely to receive accepted answers, but do not speed up the time to receive answers. Our work demonstrates the crucial role that images play by approaching the topic from a new angle and lays the foundation for future opportunities to use images to assist in tasks like generating questions and identifying question-relatedness

A Benchmark Study on Sentiment Analysis for Software Engineering Research

A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.Comment: Proceedings of 15th International Conference on Mining Software Repositories (MSR 2018

Knowledge Sharing in Platform Ecosystems through Sponsored Online Communities: The Influence of User Roles and Media Richness

Platform ecosystems are characterized by knowledge boundaries that arise between the platform owner and third-party developers. Although major platform owners such as Microsoft and SAP nurture sponsored online communities to overcome knowledge boundaries in their ecosystem, the peculiarities of such communities are yet to be examined. Drawing upon the lead user and media richness theory, we investigate how different user roles and media types influence the value of a knowledge contribution in such communities. Analyzing one million answers from the SAP Community, we uncovered that both lead users and sponsor representatives are more likely to provide valuable knowledge contributions compared to normal users. Moreover, we show that attachments, code snippets, and links significantly enhance the value of a knowledge contribution. Surprisingly, we find a strong negative moderation effect of code snippets on the contributions of sponsor representatives, but a strong positive moderation effect on the contributions of lead users

Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums

This article belongs to the Special Issue E-learning, Digital Learning, and Digital Communication Used for Education Sustainability.Discussion forums are a valuable source of information in educational platforms such as Massive Open Online Courses (MOOCs), as users can exchange opinions or even help other students in an asynchronous way, contributing to the sustainability of MOOCs even with low interaction from the instructor. Therefore, the use of the forum messages to get insights about students’ performance in a course is interesting. This article presents an automatic grading approach that can be used to assess learners through their interactions in the forum. The approach is based on the combination of three dimensions: (1) the quality of the content of the interactions, (2) the impact of the interactions, and (3) the user’s activity in the forum. The evaluation of the approach compares the assessment by experts with the automatic assessment obtaining a high accuracy of 0.8068 and Normalized Root Mean Square Error (NRMSE) of 0.1799, which outperforms previous existing approaches. Future research work can try to improve the automatic grading by the training of the indicators of the approach depending on the MOOCs or the combination with text mining techniques.This research was funded by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learn Projects under Grants TIN2017-85179-C3-1-R and PID2020-112584RB-C31, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307 and under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation), a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects, more specifically through projects InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP)