  Programming errors or exceptions are inherent in software development and
maintenance, and given today's Internet era, software developers often look at
web for finding working solutions. They make use of a search engine for
retrieving relevant pages, and then look for the appropriate solutions by
manually going through the pages one by one. However, both the manual checking
of a page's content against a given exception (and its context) and then
working an appropriate solution out are non-trivial tasks. They are even more
complex and time-consuming with the bulk of irrelevant (i.e., off-topic) and
noisy (e.g., advertisements) content in the web page. In this paper, we propose
an IDE-based and context-aware page content recommendation technique that
locates and recommends relevant sections from a given web page by exploiting
the technical details, in particular, the context of an encountered exception
in the IDE. An evaluation with 250 web pages related to 80 programming
exceptions, comparison with the only available closely related technique, and a
case study involving comparison with VSM and LSA techniques show that the
proposed technique is highly promising in terms of precision, recall and
F1-measure.
