Maolin Tang

Yanusha Mehendran, Maolin Tang, Yi Lu

Software vulnerabilities continue to undermine the reliability and security of modern systems, particularly as software complexity outpaces the capabilities of traditional detection methods. This study introduces a genetic algorithm-based method for test input generation that innovatively integrates genetic operators and adaptive learning to enhance software vulnerability detection. A key contribution is the application of the crossover operator, which facilitates exploration by searching across a broader space of potential test inputs. Complementing this, an adaptive feedback mechanism continuously learns from the system's execution behavior and dynamically guides input generation toward promising areas of the input space. Rather than relying on fixed or randomly selected inputs, the approach evolves a population of structurally valid test cases using feedback-driven selection, enabling deeper and more effective code traversal. This strategic integration of exploration and exploitation ensures that both diverse and targeted test inputs are developed over time. Evaluation was conducted across nine open-source JSON-processing libraries. The proposed method achieved substantial improvements in coverage compared to a benchmark evolutionary fuzzing method, with average gains of 39.8% in class coverage, 62.4% in method coverage, 105.0% in line coverage, 114.0% in instruction coverage, and 166.0% in branch coverage. These results highlight the method's capacity to detect deeper and more complex vulnerabilities, offering a scalable and adaptive solution to software security testing.

WeiRan Yan, MaoLin Tang, Qijun Zhao et al.

Giant pandas, stereotyped as silent animals, make significantly more vocal sounds during breeding season, suggesting that sounds are essential for coordinating their reproduction and expression of mating preference. Previous biological studies have also proven that giant panda sounds are correlated with mating results and reproduction. This paper makes the first attempt to devise an automatic method for predicting mating success of giant pandas based on their vocal sounds. Given an audio sequence of mating giant pandas recorded during breeding encounters, we first crop out the segments with vocal sound of giant pandas, and normalize its magnitude, and length. We then extract acoustic features from the audio segment and feed the features into a deep neural network, which classifies the mating into success or failure. The proposed deep neural network employs convolution layers followed by bidirection gated recurrent units to extract vocal features, and applies attention mechanism to force the network to focus on most relevant features. Evaluation experiments on a data set collected during the past nine years obtain promising results, proving the potential of audio-based automatic mating success prediction methods in assisting giant panda reproduction.