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Bug report reproduction is a crucial but time-consuming task to be carried out during mobile app maintenance. To accelerate this process, researchers have developed automated techniques for reproducing mobile app bug reports. However, due to the lack of an effective mechanism to recognize different buggy behaviors described in the report, existing work is limited to reproducing crash bug reports, or requires developers to manually analyze execution traces to determine if a bug was successfully reproduced. To address this limitation, we introduce a novel technique to automatically identify and extract the buggy behavior from the bug report and detect it during the automated reproduction process. To accommodate various buggy behaviors of mobile app bugs, we conducted an empirical study and created a standardized representation for expressing the bug behavior identified from our study. Given a report, our approach first transforms the documented buggy behavior into this standardized representation, then matches it against real-time device and UI information during the reproduction to recognize the bug. Our empirical evaluation demonstrated that our approach achieved over 90% precision and recall in generating the standardized representation of buggy behaviors. It correctly identified bugs in 83% of the bug reports and enhanced existing reproduction techniques, allowing them to reproduce four times more bug reports. 

Abstract ICON observations were used to investigate local time (LT) and latitudinal variations of thermospheric meridional winds in the middle‐high thermosphere (160–300 km) during quiet times in 2020 June and December. At middle‐low latitudes (10°S–40°N), meridional winds were predominantly equatorward in the summer hemisphere while mostly poleward in the winter hemisphere. The meridional winds showed that the diurnal variation was dominant between ∼20°N and ∼40°N, but the semi‐diurnal variation played a leading role at lower latitudes (below ∼20°N) during solstice months. Thermosphere‐Ionosphere Electrodynamics General Circulation Model reproduced the ICON observed meridional wind variations qualitatively. A model diagnostic analysis shows that the pressure gradient force dominated the semi‐diurnal variation of the winds, while the Coriolis force played a leading role in the diurnal variation in June. In December, LT variations of meridional winds were primarily driven by pressure gradient and ion drag forces. During both months, the vertical viscosity was important, tending to balance the effects of pressure gradients. Additionally, semi‐diurnal variations of low‐latitude meridional winds in June were more affected by upward propagating tides than those in December. 

Abstract The global 3‐dimensional structure of the concentric traveling ionospheric disturbances (CTIDs) triggered by 2022 Tonga volcano was reconstructed by using the 3‐dimensional computerized ionospheric tomography (3DCIT) technique and extensive global navigation satellite system (GNSS) observations. This study provides the first estimation of the CTIDs vertical wavelengths, ∼736 km, which was much larger than the gravity wave (GW) vertical wavelength, 240–400 km, estimated using ICON neutral wind observations. Notable trend with the variation of azimuth was also found in horizontal speeds at 200 and 500 km altitudes and differences between them. These results imply that (a) the global propagation of Lamb waves determined the arrival time of local ionospheric disturbances, and (b) the arriving Lamb waves caused vertical atmospheric perturbations that are not typical of GWs, resulting in local thermospheric horizontal wave propagation which is faster than the Lamb wave propagation at lower altitudes. 

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this article, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection. 

Bug report reproduction is an important, but time-consuming task carried out during mobile app maintenance. To accelerate this task, current research has proposed automated reproduction techniques that rely on a guided dynamic exploration of the app to match bug report steps with UI events in a mobile app. However, these techniques struggle to find the correct match when the bug reports have missing or inaccurately described steps. To address these limitations, we propose a new bug report reproduction technique that uses an app’s UI model to perform a global search across all possible matches between steps and UI actions and identify the most likely match while accounting for the possibility of missing or inaccurate steps. To do this, our approach redefines the bug report reproduction process as a Markov model and finds the best paths through the model using a dynamic programming based technique. We conducted an empirical evaluation on 72 real-world bug reports. Our approach achieved a 94% reproduction rate on the total bug reports and a 93% reproduction rate on bug reports with missing steps, significantly outperforming the state-of-the-art approaches. Our approach was also more effective in finding the matches from the steps to UI events than the state-of-the-art approaches. 

The execution of smart contracts on Ethereum, a public blockchain system, incurs a fee called gas fee for its computation and data storage. When programmers develop smart contracts (e.g., in the Solidity programming language), they could unknowingly write code snippets that unnecessarily cause more gas fees. These issues, or what we call gas wastes, can lead to significant monetary losses for users. This paper takes the initiative in helping Ethereum users reduce their gas fees in two key steps. First, we conduct an empirical study on gas wastes in open-source Solidity programs and Ethereum transaction traces. Second, to validate our study findings, we develop a static tool called PeCatch to effectively detect gas wastes in Solidity programs, and manually examine the Solidity compiler’s code to pinpoint implementation errors causing gas wastes. Overall, we make 11 insights and four suggestions, which can foster future tool development and programmer awareness, and fixing our detected bugs can save $0.76 million in gas fees daily. 

Continuous integration (CI) has become a popular method for automating code changes, testing, and software project delivery. However, sufficient testing prior to code submission is crucial to prevent build breaks. Additionally, testing must provide developers with quick feedback on code changes, which requires fast testing times. While regression test selection (RTS) has been studied to improve the cost-effectiveness of regression testing for lower-level tests (i.e., unit tests), it has not been applied to the testing of user interfaces (UI) in application domains such as mobile apps. UI testing at the UI level requires different techniques such as impact analysis and automated test execution. In this paper, we examine the use of RTS in CI settings for UI testing across various open-source mobile apps. Our analysis focuses on using Frequency Analysis to understand the need for RTS, Cost Analysis to evaluate the cost of impact analysis and test case selection algorithms, and Test Reuse Analysis to determine the reusability of UI test sequences for automation. The insights from this study will guide practitioners and researchers in developing advanced RTS techniques that can be adapted to CI environments for mobile apps.