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Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Obviously, functional correctness is critical for an app’s success. However, in addition to crash bugs, non-crashing functional bugs (in short as “non-crashing bugs” in this work) like inadvertent function failures, silent user data lost and incorrect display information are prevalent, even in popular, well-tested apps. These non-crashing functional bugs are usually caused by program logic errors and manifest themselves on the graphic user interfaces (GUIs). In practice, such bugs pose significant challenges in effectively detecting them because (1) current practices heavily rely on expensive, small-scale manual validation ( the lack of automation ); and (2) modern fully automated testing has been limited to crash bugs ( the lack of test oracles ). This paper fills this gap by introducing independent view fuzzing , a novel, fully automated approach for detecting non-crashing functional bugs in Android apps. Inspired by metamorphic testing, our key insight is to leverage the commonly-held independent view property of Android apps to manufacture property-preserving mutant tests from a set of seed tests that validate certain app properties. The mutated tests help exercise the tested apps under additional, adverse conditions. Any property violations indicate likely functional bugs for further manual confirmation. We have realized our approach as an automated, end-to-end functional fuzzing tool, Genie. Given an app, (1) Genie automatically detects non-crashing bugs without requiring human-provided tests and oracles (thus fully automated ); and (2) the detected non-crashing bugs are diverse (thus general and not limited to specific functional properties ), which set Genie apart from prior work. We have evaluated Genie on 12 real-world Android apps and successfully uncovered 34 previously unknown non-crashing bugs in their latest releases — all have been confirmed, and 22 have already been fixed. Most of the detected bugs are nontrivial and have escaped developer (and user) testing for at least one year and affected many app releases, thus clearly demonstrating Genie’s effectiveness. According to our analysis, Genie achieves a reasonable true positive rate of 40.9%, while these 34 non-crashing bugs could not be detected by prior fully automated GUI testing tools (as our evaluation confirms). Thus, our work complements and enhances existing manual testing and fully automated testing for crash bugs.

Five-axis flank milling is widely used in the field of aerospace and automotive industry. However, the accurate model between variety conditions of machining and the errors is difficult to establish directly. It is urgent to obtain a tool path for reducing the errors of the parts. Herein, a tool path regeneration method is proposed for five-axis flank milling of ruled surface according to the actual error distribution. The method contains three steps: First, the errors at the middle of the straight generatrix on the machined surface are calculated according to error distribution, and the corresponding normal vectors are obtained by geometric calculation. Second, multi-peaks Gaussian fitting method is utilized to make connections between parameters in the original tool path and error distribution. Finally, the regenerative tool path is obtained by offsetting original tool path. Machining experiments are performed to test the effectiveness of the proposed tool path regeneration method. The error distribution after tool path regeneration shows that the average error reduces 92.32%, with the surface roughness staying constant. Results show that the proposed tool path regeneration method is effective to improve the accuracy for five-axis flank milling.

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