Mining search topics from a code search engine usage log
Sushil Krishna Bajracharya, Cristina Videira Lopes · 2009
We present a topic modeling analysis of a year long usage log of Koders, one of the major commercial code search engines. This analysis contributes to the understanding of what users of code search engines are looking for. Observations on the prevalence of these topics among the users, and on how search and download activities vary across topics, leads to the conclusion that users who find code search engines usable are those who already know to a high level of specificity what to look for. This paper presents a general categorization of these topics that provides insights on the different ways code search engine users express their queries. The findings support the conclusion that existing code search engines provide only a subset of the various information needs of the users when compared to the categories of queries they look at.