Towards a Context-Aware IDE-Based Meta Search Engine for Recommendation about Programming Errors and Exceptions

TL;DR

This paper introduces a context-aware, IDE-integrated web search tool for programming errors that improves search relevance and developer productivity by leveraging multiple search engines and contextual information.

Contribution

It presents a novel Eclipse IDE-based search solution that incorporates context and multiple data sources to enhance error and exception recommendations.

Findings

Improved recommendation accuracy with context inclusion.

Better performance than existing approaches in recall and precision.

Low computational cost for the proposed method.

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.