  Software engineers spend a substantial amount of time using Web search to
accomplish software engineering tasks. Such search tasks include finding code
snippets, API documentation, seeking help with debugging, etc. While debugging
a bug or crash, one of the common practices of software engineers is to search
for information about the associated error or exception traces on the internet.
  In this paper, we analyze query logs from a leading commercial
general-purpose search engine (GPSE) such as Google, Yahoo! or Bing to carry
out a large scale study of software exceptions. To the best of our knowledge,
this is the first large scale study to analyze how Web search is used to find
information about exceptions. We analyzed about 1 million exception related
search queries from a random sample of 5 billion web search queries. To extract
exceptions from unstructured query text, we built a novel and high-performance
machine learning model with a F1-score of 0.82. Using the machine learning
model, we extracted exceptions from raw queries and performed popularity,
effort, success, query characteristic and web domain analysis. We also
performed programming language-specific analysis to give a better view of the
exception search behavior. These techniques can help improve existing methods,
documentation and tools for exception analysis and prediction. Further, similar
techniques can be applied for APIs, frameworks, etc.
