Clustering navigation sequences to create contexts for guiding code navigation

• To effectively guide code navigation, we propose a new approach that mines collections of code that are relevant to tasks.
• The approach clusters navigation sequences from programmers’ interaction histories.
• The approach uses the clusters to recommend the program elements that are relevant to a programmer's task at hand.
• NavClus’ recommendation accuracy is compared with that of TeamTracks’ using 4397 interaction histories.
• The comparative experiment shows that the recommendation accuracy of NavClus is twice as high as that of TeamTracks.

To guide programmer code navigation, previous approaches such as TeamTracks recommend pieces of code to visit by mining the associations between pieces of code in programmer interaction histories. However, these result in low recommendation accuracy. To create more accurate recommendations, we propose NavClus an approach that clusters navigation sequences from programmer interaction histories. NavClus automatically forms collections of code that are relevant to the tasks performed by programmers, and then retrieves the collections best matched to a programmer's current navigation path. This makes it possible to recommend the collections of code that are relevant to the programmer's given task. We compare NavClus’ recommendation accuracy with TeamTracks’ by simulating recommendations using 4397 interaction histories. The comparative experiment shows that the recommendation accuracy of NavClus is twice as high as that of TeamTracks.