Comparison of Various Heuristic Search Techniques for Finding Shortest Path

Potdar, Girish P; Thool, Ravindra C.

doi:10.5121/ijaia.2014.5405

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…To efficiently explore the app GUI, Mobolic implements A ∗ , an informed search algorithm, which is the best‐known form of BFS. () The A ∗ search algorithm combines BFS for efficiency with the uniform cost search for optimality and completeness. The key idea behind the A ∗ search algorithm is to find the shortest path leading to the target app UI.…”

Section: Discussionmentioning

confidence: 99%

“…In comparison with the listed tools, Mobolic significantly reduces search time in f‐GFG model by involving an informed search algorithm A ∗ . () In Mobolic , we implemented A ∗ search in such a way that its time complexity is

scriptO

( n ), which is linear in the number of app UIs ( n ) on the path leading to the lastly discovered app UI. Thus, A ∗ enables Mobolic to build the shortest UI‐path to quickly reach the lastly discovered app UI.…”

Section: Related Workmentioning

confidence: 99%

“…Next, we show the overall workflow of Mobolic in Algorithm 1. Generally, Mobolic can be logically divided into 3 stages: (1) preparation (lines 2-6), (2) input generation (lines 9-20), and (3) exercising of the app under test (lines [21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Control Dependence Graphmentioning

confidence: 99%

“…Mobolic explores the app GUI using our novel f‐GFG model (see Sections 2.2 and 2.3). It automatically builds the model and systematically traverses it by implementing an informed search algorithm A ∗ , which is the best‐known form of the best‐first search (BFS) algorithm . The A ∗ search algorithm combines BFS for efficiency with the uniform cost search for optimality and completeness.…”

Section: Introductionmentioning

confidence: 99%

“…It automatically builds the model and systematically traverses it by implementing an informed search algorithm A * , which is the best-known form of the best-first search (BFS) algorithm. 23 The A * search algorithm combines BFS for efficiency with the uniform cost search for optimality and completeness. The key idea behind the A * search algorithm is to find the shortest path leading to the target app UI (see Section 2.5.3).…”

mentioning

confidence: 99%

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Mobolic: An automated approach to exercising mobile application GUIs using symbiosis of online testing technique and customated input generation

et al. 2018

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Summary The increasingly prevalent use of mobile devices has raised the popularity of mobile applications. Therefore, automated testing of mobile applications has become an extremely important task. However, it is still a challenge to automatically generate tests with high coverage for mobile applications due to their specific nontrivial structure and the highly interactive nature of graphical user interfaces (GUIs). In this paper, we propose a novel automated GUI testing technique for mobile applications, namely, Mobolic. In this approach, tests with high coverage are automatically generated and executed by combining the online testing technique and customated input generation. Employing the online testing technique, Mobolic systematically explores the app GUI without falling in a loop. It generates relevant events “on the fly” that are followed by an immediate execution. In addition, involving the customated input generation, Mobolic automatically generates relevant user inputs such as user‐predefined, concrete, or random ones. We implemented Mobolic and evaluated its performance on 10 real‐world open‐source Android applications. Our experimental results show the effectiveness and efficiency of Mobolic in terms of achieved code coverage and overall exercising time.

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Section: Discussionmentioning

confidence: 99%