AGRippin: a novel search based testing technique for Android applications

作者: Domenico Amalfitano , Nicola Amatucci , Anna Rita Fasolino , Porfirio Tramontana

DOI: 10.1145/2804345.2804348

关键词:

摘要: Recent studies have shown a remarkable need for testing automation techniques in the context of mobile applications. The main contributions literature field regard such as Capture/Replay, Model Based, Learning and Random techniques. Unfortunately, only last two typologies are applicable if no previous knowledge about application under is available. able to generate effective test suites (in terms source code coverage) but they effort machine time tests quite inefficient due their redundancy. more efficient often do not reach good levels coverage. In order that both efficient, we propose this paper AGRippin, novel Search Based Testing technique founded on combination genetic hill climbing We carried out case study involving five open Android applications has demonstrated how proposed than ones generated by technique.

参考文章(27)
J. Kauth, Darrell Whitley, Genitor: a different genetic algorithm ,(1988)
Wei Yang, Mukul R. Prasad, Tao Xie, A grey-box approach for automated GUI-model generation of mobile applications fundamental approaches to software engineering. pp. 250- 265 ,(2013) , 10.1007/978-3-642-37057-1_19
Gilbert Syswerda, A Study of Reproduction in Generational and Steady-State Genetic Algorithms Foundations of Genetic Algorithms. ,vol. 1, pp. 94- 101 ,(1991) , 10.1016/B978-0-08-050684-5.50009-4
Heinz Mühlenbein, How Genetic Algorithms Really Work: Mutation and Hillclimbing. parallel problem solving from nature. pp. 15- 26 ,(1992)
James E. Baker, Adaptive Selection Methods for Genetic Algorithms international conference on genetic algorithms. pp. 101- 111 ,(1985)
Mona Erfani Joorabchi, Ali Mesbah, Philippe Kruchten, Real Challenges in Mobile App Development 2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement. pp. 15- 24 ,(2013) , 10.1109/ESEM.2013.9
M. Srinivas, L.M. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithms systems man and cybernetics. ,vol. 24, pp. 656- 667 ,(1994) , 10.1109/21.286385
Henry Muccini, Patrizio Esposito, Antonio Di Francesco, Software testing of mobile applications: challenges and future research directions automation of software test. pp. 29- 35 ,(2012) , 10.5555/2663608.2663615
Aravind Machiry, Rohan Tahiliani, Mayur Naik, Dynodroid: an input generation system for Android apps foundations of software engineering. pp. 224- 234 ,(2013) , 10.1145/2491411.2491450
Riyadh Mahmood, Nariman Mirzaei, Sam Malek, EvoDroid: segmented evolutionary testing of Android apps foundations of software engineering. pp. 599- 609 ,(2014) , 10.1145/2635868.2635896