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Clustering navigation sequences to create contexts for guiding code navigation
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AI
DOI：10.1016/j.jss.2013.03.103.png)
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Abstract
En 中文
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. (c) 2013 Elsevier Inc. All rights reserved.
Keywords:
Code navigation
Programmer interaction histories
Data clustering techniques
Data stream mining
Context aware code recommender
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4.1
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5.4K
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