  Web search plays an integral role in software engineering (SE) to help with
various tasks such as finding documentation, debugging, installation, etc. In
this work, we present the first large-scale analysis of web search behavior for
SE tasks using the search query logs from Bing, a commercial web search engine.
First, we use distant supervision techniques to build a machine learning
classifier to extract the SE search queries with an F1 score of 93%. We then
perform an analysis on one million search sessions to understand how software
engineering related queries and sessions differ from other queries and
sessions. Subsequently, we propose a taxonomy of intents to identify the
various contexts in which web search is used in software engineering. Lastly,
we analyze millions of SE queries to understand the distribution, search
metrics and trends across these SE search intents. Our analysis shows that SE
related queries form a significant portion of the overall web search traffic.
Additionally, we found that there are six major intent categories for which web
search is used in software engineering. The techniques and insights can not
only help improve existing tools but can also inspire the development of new
tools that aid in finding information for SE related tasks.
