  During maintenance, software developers deal with numerous change requests
that are written in an unstructured fashion using natural language. Such
natural language texts illustrate the change requirement involving various
domain related concepts. Software developers need to find appropriate search
terms from those concepts so that they could locate the possible locations in
the source code using a search technique. Once such locations are identified,
they can implement the requested changes there. Studies suggest that developers
often perform poorly in coming up with good search terms for a change task. In
this paper, we propose a novel technique--STRICT--that automatically identifies
suitable search terms for a software change task by analyzing its task
description using two information retrieval (IR) techniques-- TextRank and
POSRank. These IR techniques determine a term's importance based on not only
its co-occurrences with other important terms but also its syntactic
relationships with them. Experiments using 1,939 change requests from eight
subject systems report that STRICT can identify better quality search terms
than baseline terms from 52%--62% of the requests with 30%--57% Top-10
retrieval accuracy which are promising. Comparison with two state-of-the-art
techniques not only validates our empirical findings and but also demonstrates
the superiority of our technique.
