Shamima Yeasmin

Mohammad Masudur Rahman, Shamima Yeasmin, Chanchal K. Roy

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.

Mohammad Masudur Rahman, Shamima Yeasmin, Chanchal K. Roy

Traditional web search forces the developers to leave their working environments and look for solutions in the web browsers. It often does not consider the context of their programming problems. The context-switching between the web browser and the working environment is time-consuming and distracting, and the keyword-based traditional search often does not help much in problem solving. In this paper, we propose an Eclipse IDE-based web search solution that collects the data from three web search APIs-- Google, Yahoo, Bing and a programming Q & A site-- Stack Overflow. It then provides search results within IDE taking not only the content of the selected error into account but also the problem context, popularity and search engine recommendation of the result links. Experiments with 25 run time errors and exceptions show that the proposed approach outperforms the keyword-based search approaches with a recommendation accuracy of 96%. We also validate the results with a user study involving five prospective participants where we get a result agreement of 64.28%. While the preliminary results are promising, the approach needs to be further validated with more errors and exceptions followed by a user study with more participants to establish itself as a complete IDE-based web search solution.