How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms

  • Authors:

  • Annibale Panichella;Bogdan Dit;Rocco Oliveto;Massimiliano Di Penta;Denys Poshyvanyk;Andrea De Lucia

  • Affiliations:

  • University of Salerno, Italy;College of William and Mary, USA;University of Molise, Italy;University of Sannio, Italy;College of William and Mary, USA;University of Salerno, Italy

  • Venue:
  • Proceedings of the 2013 International Conference on Software Engineering
  • Year:
  • 2013

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Abstract

Information Retrieval (IR) methods, and in particular topic models, have recently been used to support essential software engineering (SE) tasks, by enabling software textual retrieval and analysis. In all these approaches, topic models have been used on software artifacts in a similar manner as they were used on natural language documents (e.g., using the same settings and parameters) because the underlying assumption was that source code and natural language documents are similar. However, applying topic models on software data using the same settings as for natural language text did not always produce the expected results. Recent research investigated this assumption and showed that source code is much more repetitive and predictable as compared to the natural language text. Our paper builds on this new fundamental finding and proposes a novel solution to adapt, configure and effectively use a topic modeling technique, namely Latent Dirichlet Allocation (LDA), to achieve better (acceptable) performance across various SE tasks. Our paper introduces a novel solution called LDA-GA, which uses Genetic Algorithms (GA) to determine a near-optimal configuration for LDA in the context of three different SE tasks: (1) traceability link recovery, (2) feature location, and (3) software artifact labeling. The results of our empirical studies demonstrate that LDA-GA is ableto identify robust LDA configurations, which lead to a higher accuracy on all the datasets for these SE tasks as compared to previously published results, heuristics, and the results of a combinatorial search.