Stack Overflow Posts Research Articles

How Often and What StackOverflow Posts Do Developers Reference in Their GitHub Projects?

How Often and What StackOverflow Posts Do Developers Reference in Their GitHub Projects?

How Often and What StackOverflow Posts Do Developers Reference in Their GitHub Projects?

How Often and What StackOverflow Posts Do Developers Reference in Their GitHub Projects?

Mining structured data in natural language artifacts with island parsing

Software repositories typically store data composed of structured and unstructured parts. Researchers mine this data to empirically validate research ideas and to support practitioners' activities. Structured data (e.g., source code) has a formal syntax and is straightforward to analyze; unstructured data (e.g., documentation) is a mix of natural language, noise, and snippets of structured data, and it is harder to analyze. Especially the structured content (e.g., code snippets) in unstructured data contains valuable information. Researchers have proposed several approaches to recognize, extract, and analyze structured data embedded in natural language. We analyze these approaches and investigate their drawbacks. Subsequently, we present two novel methods, based on scannerless generalized LR (SGLR) and Parsing Expression Grammars (PEGs), to address these drawbacks and to mine structured fragments within unstructured data. We validate and compare these approaches on development emails and Stack Overflow posts with Java code fragments. Both approaches achieve high precision and recall values, but the PEG-based one achieves better computational performances and simplicity in engineering.

What Do Developers Use the Crowd For? A Study Using Stack Overflow

Stack Overflow relies on the crowd to construct quality developer-related knowledge. To determine what developers use this knowledge for, researchers analyzed 1,414 Stack Overflow-related code commits. The developers used this knowledge to support development tasks and collect user feedback. The researchers also studied Stack Overflow posts' helpfulness and timeliness. The crowd was the most helpful on topics such as development tools and programming languages. The questions that took the longest to resolve were related to Web frameworks. The study findings can help developers better understand how to effectively use Stack Overflow, can help Stack Overflow designers improve their platform, and can help the research community understand Stack Overflow's strengths and weaknesses as a development tool. This article is part of a special issue on Crowdsourcing for Software Engineering.

Generating API Call Rules from Version History and Stack Overflow Posts

Researchers have shown that related functions can be mined from groupings of functions found in the version history of a system. Our first contribution is to expand this approach to a community of applications and set of similar applications. Android developers use a set of application programming interface (API) calls when creating apps. These API calls are used in similar ways across multiple applications. By clustering co-changing API calls used by 230 Android apps across 12k versions, we are able to predict the API calls that individual app developers will use with an average precision of 75% and recall of 22%. When we make predictions from the same category of app, such as Finance, we attain precision and recall of 81% and 28%, respectively. Our second contribution can be characterized as “programmers who discussed these functions were also interested in these functions.” Informal discussions on Stack Overflow provide a rich source of information about related API calls as developers provide solutions to common problems. By grouping API calls contained in each positively voted answer posts, we are able to create rules that predict the calls that app developers will use in their own apps with an average precision of 66% and recall of 13%. For comparison purposes, we developed a baseline by clustering co-changing API calls for each individual app and generated association rules from them. The baseline predicts API calls used by app developers with a precision and recall of 36% and 23%, respectively.

What Security Questions Do Developers Ask? A Large-Scale Study of Stack Overflow Posts

Security has always been a popular and critical topic. With the rapid development of information technology, it is always attracting people’s attention. However, since security has a long history, it covers a wide range of topics which change a lot, from classic cryptography to recently popular mobile security. There is a need to investigate security-related topics and trends, which can be a guide for security researchers, security educators and security practitioners. To address the above-mentioned need, in this paper, we conduct a large-scale study on security-related questions on Stack Overflow. Stack Overflow is a popular on-line question and answer site for software developers to communicate, collaborate, and share information with one another. There are many different topics among the numerous questions posted on Stack Overflow and security-related questions occupy a large proportion and have an important and significant position. We first use two heuristics to extract from the dataset the questions that are related to security based on the tags of the posts. And then we use an advanced topic model, Latent Dirichlet Allocation (LDA) tuned using Genetic Algorithm (GA), to cluster different security-related questions based on their texts. After obtaining the different topics of security-related questions, we use their metadata to make various analyses. We summarize all the topics into five main categories, and investigate the popularity and difficulty of different topics as well. Based on the results of our study, we conclude several implications for researchers, educators and practitioners.

Linking Issue Tracker with Q&A Sites for Knowledge Sharing across Communities

Collaborative development communities and knowledge sharing communities are highly correlated and mutually complementary. The knowledge sharing between these two types of open source communities can be very beneficial to both of them. However, it is a great challenge to automate this process. Current studies mainly focus on knowledge acquisition in one type of community, and few of them have tackle this problem efficiently. In this paper we take Android Issue Tracker and Stack Overflow as a case to study the mutual knowledge sharing between them. We propose an automatic approach by integrating semantic similarity with temporal locality between Android issues and Stack Overflow posts based on the internal citation-graph to reveal the potential associations between them. Our approach explores the internal citations in communities for closely related posts or issues clustering, exploits the rich semantics in fine-grained information of issues and posts for associations building, and leverages the temporal correlations between issues and posts in-depth for associations ranking. Extensive experiments show that the precision of our approach reaches 62.51 percent for top 10 recommendations when recommending Stack Overflow posts to Android issues, and 66.83 percent in reverse.