Yanfu Yan

Impact analysis (IA) is a critical software maintenance task that identifies the effects of a given set of code changes on a larger software project with the intention of avoiding potential adverse effects. IA is a cognitively challenging task that involves reasoning about the abstract relationships between various code constructs. Given its difficulty, researchers have worked to automate IA with approaches that primarily use coupling metrics as a measure of the “connectedness” of different parts of a software project. Many of these coupling metrics rely on static, dynamic, or evolutionary information and are based on heuristics that tend to be brittle, require expensive execution analysis, or large histories of co-changes to accurately estimate impact sets. In this paper, we introduce a novel IA approach, called Athena , that combines a software system’s dependence graph information with a conceptual coupling approach that uses advances in deep representation learning for code without the need for change histories and execution information. Previous IA benchmarks are small, containing less than ten software projects, and suffer from tangled commits, making it difficult to measure accurate results. Therefore, we constructed a large-scale IA benchmark, from 25 open-source software projects, that utilizes fine-grained commit information from bug fixes. On this new benchmark, our best performing approach configuration achieves an mRR, mAP, and HIT@10 score of 60.32%, 35.19%, and 81.48%, respectively. Through various ablations and qualitative analyses, we show that Athena ’s novel combination of program dependence graphs and conceptual coupling information leads it to outperform a simpler baseline by 10.34%, 9.55%, and 11.68% with statistical significance.

Video-based bug reports are increasingly being used to document bugs for programs centered around a graphical user interface (GUI). However, developing automated techniques to manage video-based reports is challenging as it requires identifying and understanding often nuanced visual patterns that capture key information about a reported bug. In this paper, we aim to overcome these challenges by advancing the bug report management task of duplicate detection for video-based reports. To this end, we introduce a new approach, called Janus, that adapts the scene-learning capabilities of vision transformers to capture subtle visual and textual patterns that manifest on app UI screens --- which is key to differentiating between similar screens for accurate duplicate report detection. Janus also makes use of a video alignment technique capable of adaptive weighting of video frames to account for typical bug manifestation patterns. In a comprehensive evaluation on a benchmark containing 7,290 duplicate detection tasks derived from 270 video-based bug reports from 90 Android app bugs, the best configuration of our approach achieves an overall mRR/mAP of 89.8%/84.7%, and for the large majority of duplicate detection tasks, outperforms prior work by ≈9% to a statistically significant degree. Finally, we qualitatively illustrate how the scene-learning capabilities provided by Janus benefits its performance.

The experiments focused on the influence of magnesium and titanium as additional alloying elements on the microstructure and electrochemical behavior of Al‐Zn‐In sacrificial anodes. The electrochemical behavior of the aluminum sacrificial anode with 3 wt.% sodium chloride solution was studied by electrochemical impedance spectroscopy (EIS) tests. It was found that a microstructure with few precipitates and refined grains could be achieved by adding 1 wt.% Mg and 0.05 wt.% Ti to the Al‐Zn‐In alloy, resulting in the improved current capacity and efficiency of the alloy. The equivalent circuit based on the EIS experimental data revealed less corrosion and lower adsorbed corrosion production on the surface of the aluminum alloy with a combination of 1 wt.% Mg and 0.05 wt.% Ti, which suggested that the corrosion behavior seemed to be strongly related to the presence of precipitate particles in the aluminum alloy, and moderate amounts of precipitate particles could be beneficial to the electrochemical performance of the aluminum alloy sacrificial anode.