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Publication Detail
Constructing Search Spaces for Search-Based Software Testing Using Neural Networks
  • Publication Type:
    Conference
  • Authors:
    Joffe L, Clark D
  • Publication date:
    03/08/2019
  • Pagination:
    27, 41
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    11664 LNCS
  • ISBN-13:
    9783030274542
  • Status:
    Published
  • Print ISSN:
    0302-9743
Abstract
© Springer Nature Switzerland AG 2019. A central requirement for any Search-Based Software Testing (SBST) technique is a convenient and meaningful fitness landscape. Whether one follows a targeted or a diversification driven strategy, a search landscape needs to be large, continuous, easy to construct and representative of the underlying property of interest. Constructing such a landscape is not a trivial task often requiring a significant manual effort by an expert. We present an approach for constructing meaningful and convenient fitness landscapes using neural networks (NN) – for targeted and diversification strategies alike. We suggest that output of an NN predictor can be interpreted as a fitness for a targeted strategy. The NN is trained on a corpus of execution traces and various properties of interest, prior to searching. During search, the trained NN is queried to predict an estimate of a property given an execution trace. The outputs of the NN form a convenient search space which is strongly representative of a number of properties. We believe that such a search space can be readily used for driving a search towards specific properties of interest. For a diversification strategy, we propose the use of an autoencoder; a mechanism for compacting data into an n-dimensional “latent” space. In it, datapoints are arranged according to the similarity of their salient features. We show that a latent space of execution traces possesses characteristics of a convenient search landscape: it is continuous, large and crucially, it defines a notion of similarity to arbitrary observations.
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