Towards aiding within-patch information foraging by end-user programmers

Many tools help professional programmers with the difficult problem of finding information during code maintenance. The empirical success of these tools can be explained by Information Foraging Theory (IFT) which predicts how a person seeks information by navigating through an information system based on the visual weight of information features presented to the person. Motivated by the success of these tools, we investigated the reasonable expectation that end-user programmers would likewise benefit from tools that increased the relative visual weight of important information features. We prototyped and evaluated two tools, each of which uses an existing algorithm to identify the most important lines of code. One prototype highlights important lines of code; the other prototype hides unimportant lines of code. An empirical study revealed that increasing the relative weight of important information features by highlighting did positively impact the amount of information foraged and the rate of information gained; on the other hand, decreasing the relative weight of unimportant information features by hiding had a modest negative impact. These results reveal opportunities for enhancing existing IFT-based foraging models and applying them to design more effective end-user programming tools for coding, debugging, and code reuse.