ContentSuggest

During development and maintenance of a software product, software developers often search for relevant information in the web about an encountered error or exception, where they manually check the web pages returned by a search engine in order to extract a working solution. Both manual checking of the page content against the exception (and its context) and extracting an appropriate solution are non-trivial tasks. They are even more complicated with the bulk of noisy (e.g., advertisements, navigation menus) and irrelevant content in the web page. In this paper, we propose an IDE-based context-aware page content recommendation approach that not only returns all the noise-free (i.e., main) sections of a web page but also identifies the relevant sections of the page. The approach exploits the encountered exception and its context in the IDE in order to recommend the relevant sections of the webpage resulted from an IDE-based web search.