NDLI: AGRippin: a novel search based testing technique for Android applications

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AGRippin: a novel search based testing technique for Android applications

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AGRippin: a novel search based testing technique for Android applications

Content Provider	ACM Digital Library
Author	Amalfitano, Domenico Tramontana, Porfirio Amatucci, Nicola Fasolino, Anna Rita
Abstract	Recent studies have shown a remarkable need for testing automation techniques in the context of mobile applications. The main contributions in literature in the field of testing automation regard techniques such as Capture/Replay, Model Based, Model Learning and Random techniques. Unfortunately, only the last two typologies of techniques are applicable if no previous knowledge about the application under testing is available. Random techniques are able to generate effective test suites (in terms of source code coverage) but they need a remarkable effort in terms of machine time and the tests they generate are quite inefficient due to their redundancy. Model Learning techniques generate more efficient test suites but often they do not not reach good levels of coverage. In order to generate test suites that are both effective and efficient, we propose in this paper AGRippin, a novel Search Based Testing technique founded on the combination of genetic and hill climbing techniques. We carried out a case study involving five open source Android applications that has demonstrated how the proposed technique is able to generate test suites that are more effective and efficient than the ones generated by a Model Learning technique. .
Starting Page	5
Ending Page	12
Page Count	8
File Format	PDF
ISBN	9781450338158
DOI	10.1145/2804345.2804348
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Search based testing Android Genetic algorithms
Content Type	Text
Resource Type	Article

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