The Evolution of Stack Overflow Posts: Reconstruction and Analysis
This work provides a dataset and analysis for software developers and researchers to study content evolution on Stack Overflow, but it is incremental as it builds on existing data dumps with improved extraction methods.
The researchers tackled the problem of analyzing how Stack Overflow posts evolve over time by creating SOTorrent, an open dataset that reconstructs version histories of text and code blocks, and found that post edits are typically small, occur soon after creation, and rarely involve code changes without text updates.
Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of code snippets and free-form text on a wide variety of topics. Like other software artifacts, questions and answers on SO evolve over time, for example when bugs in code snippets are fixed, code is updated to work with a more recent library version, or text surrounding a code snippet is edited for clarity. To be able to analyze how content on SO evolves, we built SOTorrent, an open dataset based on the official SO data dump. SOTorrent provides access to the version history of SO content at the level of whole posts and individual text or code blocks. It connects SO posts to other platforms by aggregating URLs from text blocks and comments, and by collecting references from GitHub files to SO posts. In this paper, we describe how we built SOTorrent, and in particular how we evaluated 134 different string similarity metrics regarding their applicability for reconstructing the version history of text and code blocks. Based on different analyses using the dataset, we present: (1) insights into the evolution of SO posts, e.g., that post edits are usually small, happen soon after the initial creation of the post, and that code is rarely changed without also updating the surrounding text; (2) a qualitative study investigating the close relationship between post edits and comments, (3) a first analysis of code clones on SO together with an investigation of possible licensing risks. Finally, since the initial presentation of the dataset, we improved the post block extraction and our predecessor matching strategy.