Joffe, L;
Clark, D;
(2019)
Constructing Search Spaces for Search-Based Software Testing Using Neural Networks.
In: Nejati, S and Gay, G, (eds.)
Lecture Notes in Computer Science.
(pp. pp. 27-41).
Springer: Tallinn, Estonia.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Constructing Search Spaces for Search-Based Software Testing Using Neural Networks |
| Event: | 11th International Symposium SSBSE: International Symposium on Search Based Software Engineering |
| ISBN-13: | 9783030274542 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-27455-9_3 |
| Publisher version: | https://doi.org/10.1007/978-3-030-27455-9_3 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Search-Based Software Testing · Software Engineering · Fitness Function · Machine Learning · Neural Networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10094843 |
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