Instance Generator and Problem Representation to Improve Object Oriented Code Coverage. Sakti, A., Pesant, G., & Guéhéneuc, Y. Transactions on Software Engineering (TSE), 40(99):1–22, IEEE CS Press, October, 2014. 22 pages.
Paper abstract bibtex Search-based approaches have been extensively applied to solve the problem of software test-data generation. Yet, testdata generation for object-oriented programming (OOP) is challenging due to the features of OOP, e.g., abstraction, encapsulation, and visibility that prevent direct access to some parts of the source code. To address this problem we present a new automated search-based software test-data generation approach that achieves high code coverage for unit-class testing. We first describe how we structure the test-data generation problem for unit-class testing to generate relevant sequences of method calls. Through a static analysis, we consider only methods or constructors changing the state of the class-under-test or that may reach a test target. Then we introduce a generator of instances of classes that is based on a family of means-of-instantiation including subclasses and external factory methods. It also uses a seeding strategy and a diversification strategy to increase the likelihood to reach a test target. Using a search heuristic to reach all test targets at the same time, we implement our approach in a tool, JTExpert, that we evaluate on more than a hundred Java classes from different open-source libraries. JTExpert gives better results in terms of search time and code coverage than the state of the art, EvoSuite, which uses traditional techniques.
@ARTICLE{Sakti14-TSE-JTExpert,
author = {Abdelilah Sakti and Gilles Pesant and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc},
title = {Instance Generator and Problem Representation to Improve Object Oriented Code Coverage},
journal = {Transactions on Software Engineering ({TSE})},
year = {2014},
month = {October},
volume = {40},
number = {99},
pages = {1--22},
note = {22 pages.},
abstract = {
Search-based approaches have been extensively applied to solve the problem of
software test-data generation. Yet, testdata generation for object-oriented
programming (OOP) is challenging due to the features of OOP, e.g.,
abstraction, encapsulation, and visibility that prevent direct access to some
parts of the source code. To address this problem we present a new automated
search-based software test-data generation approach that achieves high code
coverage for unit-class testing. We first describe how we structure the
test-data generation problem for unit-class testing to generate relevant
sequences of method calls. Through a static analysis, we consider only
methods or constructors changing the state of the class-under-test or that
may reach a test target. Then we introduce a generator of instances of
classes that is based on a family of means-of-instantiation including
subclasses and external factory methods. It also uses a seeding strategy and
a diversification strategy to increase the likelihood to reach a test target.
Using a search heuristic to reach all test targets at the same time, we
implement our approach in a tool, JTExpert, that we evaluate on more than a
hundred Java classes from different open-source libraries. JTExpert gives
better results in terms of search time and code coverage than the state of
the art, EvoSuite, which uses traditional techniques.},
editor = {Matthew B.\ Dwyer},
grant = {NSERC DG and FQRNT team grant},
keywords = {Test case generation ; TSE},
kind = {RIAS},
language = {english},
publisher = {IEEE CS Press},
url = {http://www.ptidej.net/publications/documents/TSE14b.doc.pdf}
}
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