ContentSuggest

Abstract: 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 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 approach that locates and recommends relevant sections of a web page by exploiting the technical details, in particular the context, of an encountered exception in the IDE. A preliminary evaluation with 250 web pages related to 80 programming errors and exceptions and comparison against one existing approach show that the proposed approach is highly promising in terms of precision, recall and F1-measure.