| Reference : Test Suite Generation with the Many Independent Objective (MIO) Algorithm |
| Scientific journals : Article | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/35836 | |||
| Test Suite Generation with the Many Independent Objective (MIO) Algorithm | |
| English | |
| Arcuri, Andrea [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Dec-2018 | |
| Information and Software Technology | |
| Elsevier Science | |
| 104 | |
| December | |
| 195-206 | |
| Yes (verified by ORBilu) | |
| International | |
| 0950-5849 | |
| 1873-6025 | |
| Amsterdam | |
| Netherlands | |
| [en] Context:
Automatically generating test suites is intrinsically a multi-objective problem, as any of the testing targets (e.g, statements to execute or mutants to kill) is an objective on its own. Test suite generation has peculiarities that are quite different from other more regular optimisation problems. For example, given an existing test suite, one can add more tests to cover the remaining objectives. One would like the smallest number of small tests to cover as many objectives as possible, but that is a secondary goal compared to covering those targets in the first place. Furthermore, the amount of objectives in software testing can quickly become unmanageable, in the order of (tens/hundreds of) thousands, especially for system testing of industrial size systems. Objective: To overcome these issues, different techniques have been proposed, like for example the Whole Test Suite (WTS) approach and the Many-Objective Sorting Algorithm (MOSA). However, those techniques might not scale well to very large numbers of objectives and limited search budgets (a typical case in system testing). In this paper, we propose a novel algorithm, called Many Independent Objective (MIO) algorithm. This algorithm is designed and tailored based on the specific properties of test suite generation. Method: An empirical study was carried out for test suite generation on a series of artificial examples and seven RESTful API web services. The \evo system test generation tool was used, where MIO, MOSA, WTS and random search were compared. Results: The presented MIO algorithm resulted having the best overall performance, but was not the best on all problems. Conclusion: The novel presented MIO algorithm is a step forward in the automation of test suite generation for system testing. However, there are still properties of system testing that can be exploited to achieve even better results. | |
| http://hdl.handle.net/10993/35836 | |
| 10.1016/j.infsof.2018.05.003 | |
| FnR ; FNR3949772 > Lionel Briand > VVLAB > Validation and Verification Laboratory > 01/01/2012 > 31/07/2018 > 2010 |
| File(s) associated to this reference | ||||||||||||||
|
Fulltext file(s):
| ||||||||||||||
All documents in ORBilu are protected by a user license.
