Title: Towards a Context-Aware IDE-Based Meta Search Engine for Recommendation about Programming Errors and Exceptions,
Abstract: Study shows that software developers spend about 19% of their time looking
for information in the web during software development and maintenance.
Traditional web search forces them to leave the working environment (e.g., IDE)
and look for information in the web browser. It also does not consider the
context of the problems that the developers search solutions for. The frequent
switching between web browser and the IDE is both time-consuming and
distracting, and the keyword-based traditional web search often does not help
much in problem solving. In this paper, we propose an Eclipse IDE-based web
search solution that exploits the APIs provided by three popular web search
engines-- Google, Yahoo, Bing and a popular programming Q & A site, Stack
Overflow, and captures the content-relevance, context-relevance, popularity and
search engine confidence of each candidate result against the encountered
programming problems. Experiments with 75 programming errors and exceptions
using the proposed approach show that inclusion of different types of context
information associated with a given exception can enhance the recommendation
accuracy of a given exception. Experiments both with two existing approaches
and existing web search engines confirm that our approach can perform better
than them in terms of recall, mean precision and other performance measures
with little computational cost.