  Search is an integral part of a software development process. Developers
often use search engines to look for information during development, including
reusable code snippets, API understanding, and reference examples. Developers
tend to prefer general-purpose search engines like Google, which are often not
optimized for code related documents and use search strategies and ranking
techniques that are more optimized for generic, non-code related information.
In this paper, we explore whether a general purpose search engine like Google
is an optimal choice for code-related searches. In particular, we investigate
whether the performance of searching with Google varies for code vs. non-code
related searches. To analyze this, we collect search logs from 310 developers
that contains nearly 150,000 search queries from Google and the associated
result clicks. To differentiate between code-related searches and non-code
related searches, we build a model which identifies the code intent of queries.
Leveraging this model, we build an automatic classifier that detects a code and
non-code related query. We confirm the effectiveness of the classifier on
manually annotated queries where the classifier achieves a precision of 87%, a
recall of 86%, and an F1-score of 87%. We apply this classifier to
automatically annotate all the queries in the dataset. Analyzing this dataset,
we observe that code related searching often requires more effort (e.g., time,
result clicks, and query modifications) than general non-code search, which
indicates code search performance with a general search engine is less
effective.
