What are developers talking about? An analysis of topics and trends in Stack Overflow

By Anton Barua, Stephen W. Thomas, Ahmed E. Hassan. Empirical Software Engineering, 2014

617 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Easy over hard: a case study on deep learning (2017, methodology): “LDA is a widely used technique in soware engineering eld to nd related topics within unstructured text, like topic analytics on Stack Overow [5] and source code analysis [10].”
A comprehensive study on deep learning bug characteristics (2019, background): “[3] analyzed the textual content of Stack Overflow discussions to understand the thoughts and needs of developers.”
You Get Where You're Looking for: The Impact of Information Sources on Code Security (2016, background): “Much of this research focused on what types of questions are asked, which are most likely to be answered, and who does the asking and answering [5], [6], [27], [38]–[40].”
NIRMAL: Automatic identification of software relevant tweets leveraging language model (2015, methodology): “applied LDA to discover topics and trends present in questions and answers on StackOverflow [3].”
An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples (2019, background): “Software developers benefit from Stack Overflow posts, while programming [8], [12], [13], [28], [29], and read about the technologies and tools needed for development [30], [31], [32].”