ICSME 2015

2015 IEEE 31st International Conference on Software Maintenance and Evolution (ICSME), September 29 – October 1, 2015, Bremen, Germany

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Maintenance and Analysis
Early Research Achievements Track
GW2 B2890, Chairs: Ferenc Rudolf and Giuseppe Scanniello
Exploring the Use of Deep Learning for Feature Location
Christopher S. Corley, Kostadin Damevski, and Nicholas A. Kraft
(University of Alabama, USA; Virginia Commonwealth University, USA; ABB Corporate Research, USA)
Supplementary Material
Abstract: Deep learning models can infer complex patterns present in natural language text. Relative to n-gram models, deep learning models can capture more complex statistical patterns based on smaller training corpora. In this paper we explore the use of a particular deep learning model, document vectors (DVs), for feature location. DVs seem well suited to use with source code, because they both capture the influence of context on each term in a corpus and map terms into a continuous semantic space that encodes semantic relationships such as synonymy. We present preliminary results that show that a feature location technique (FLT) based on DVs can outperform an analogous FLT based on latent Dirichlet allocation (LDA) and then suggest several directions for future work on the use of deep learning models to improve developer effectiveness in feature location.


Time stamp: 2019-12-15T17:10:06+01:00