An Automatic Test Data Generation Tool using Machine Learning

Ciprian Paduraru, Marius-Constantin Melemciuc

2018

Abstract

This paper discusses an open source tool that is capable to assist users in generating automatic test data for multiple programs under test. The tool works by clustering inputs data from a corpus folder and producing generative models for each of the clusters. The models have a recurrent neural network structure and their training and sampling are parallelized with Tensorflow. As features, the tool supports online updating of the corpus folder and the already trained models, and supports any kind of program under test or input file example. There is no manual effort for users, other than customizing per cluster parameters for optimizations or using function hooks that they could use through a data structure, which acts as an expert system. The evaluation section shows the efficiency of both learning and code coverage using some concrete programs and new tests sampling methods.

Paper Citation

in Harvard Style

Paduraru C. and Melemciuc M. (2018). An Automatic Test Data Generation Tool using Machine Learning.In Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-320-9, pages 472-481. DOI: 10.5220/0006836604720481

in Bibtex Style

@conference{icsoft18,
author={Ciprian Paduraru and Marius-Constantin Melemciuc},
title={An Automatic Test Data Generation Tool using Machine Learning},
booktitle={Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2018},
pages={472-481},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006836604720481},
isbn={978-989-758-320-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - An Automatic Test Data Generation Tool using Machine Learning
SN - 978-989-758-320-9
AU - Paduraru C.
AU - Melemciuc M.
PY - 2018
SP - 472
EP - 481
DO - 10.5220/0006836604720481