eprintid: 10094843
rev_number: 19
eprint_status: archive
userid: 608
dir: disk0/10/09/48/43
datestamp: 2020-04-16 11:53:13
lastmod: 2021-09-25 23:35:26
status_changed: 2020-04-16 11:53:13
type: proceedings_section
metadata_visibility: show
creators_name: Joffe, L
creators_name: Clark, D
title: Constructing Search Spaces for Search-Based Software Testing Using Neural Networks
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Search-Based Software Testing · Software Engineering · Fitness Function · Machine Learning · Neural Networks
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2019-08-03
date_type: published
publisher: Springer
official_url: https://doi.org/10.1007/978-3-030-27455-9_3
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1703896
doi: 10.1007/978-3-030-27455-9_3
isbn_13: 9783030274542
lyricists_name: Clark, David
lyricists_name: Joffe, Leonid
lyricists_id: DCLAR93
lyricists_id: LJOFF32
actors_name: Clark, David
actors_id: DCLAR93
actors_role: owner
full_text_status: public
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 11664
place_of_pub: Tallinn, Estonia
pagerange: 27-41
event_title: 11th International Symposium SSBSE: International Symposium on Search Based Software Engineering
book_title: Lecture Notes in Computer Science
editors_name: Nejati, S
editors_name: Gay, G
citation:        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.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10094843/1/main.pdf