Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2021

Abstract

Search-based software testing (SBST) generates tests using search algorithms guided by measurements gauging how far a test case is away from exercising a coverage goal. The effectiveness of SBST largely depends on the continuity and monotonicity of the fitness landscape decided by these measurements and the search operators. Unfortunately, the fitness landscape is challenging when the function under test takes object inputs, as classical measurements hardly provide guidance for constructing legitimate object inputs. To overcome this problem, we propose test seeds, i.e., test code skeletons of legitimate objects which enable the use of classical measurements. Given a target branch in a function under test, we first statically analyze the function to build an object construction graph that captures the relation between the operands of the target method and the states of their relevant object inputs. Based on the graph, we synthesize test template code where each “slot” is a mutation point for the search algorithm. This approach can be seamlessly integrated with existing SBST algorithms, and we implemented EvoObj on top of the well-known EvoSuite unit test generation tool. Our experiments show that EvoObj outperforms EvoSuite with statistical significance on 2,750 methods taken from 103 open source Java projects using state-of-the-art SBST algorithms.

Keywords

object oriented, software testing, search-based, code synthesis

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Athens, Greece, 2021 August 23-28

First Page

1068

Last Page

1080

Identifier

10.1145/3468264.3468619

Publisher

ACM

City or Country

New York

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