{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:28:27Z","timestamp":1766122107159,"version":"3.48.0"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>High-quality test cases are vital for ensuring software reliability and security. However, existing symbolic execution tools generally rely on single-path search strategies, have limited feature extraction capability, and exhibit unstable model predictions. These limitations make them prone to local optima in complex or cross-scenario tasks and hinder their ability to balance testing quality with execution efficiency. To address these challenges, this paper proposes a Deep Active Ensemble Learning Framework for symbolic execution path exploration. During training, the framework integrates active learning with ensemble learning to reduce annotation costs and improve model robustness, while constructing a heterogeneous model pool to leverage complementary model strengths. In the testing stage, a dynamic ensemble mechanism based on sample similarity adaptively selects the optimal predictive model to guide symbolic path exploration. In addition, a gated graph neural network is employed to extract structural and semantic features from the control flow graph, improving program behavior understanding. To balance efficiency and coverage, a dynamic sliding window mechanism based on branch density enables real-time window adjustment under path complexity awareness. Experimental results on multiple real-world benchmark programs show that the proposed framework detects up to 16 vulnerabilities and achieves a cumulative 27.5% increase in discovered execution paths in hybrid fuzzing. Furthermore, the dynamic sliding window mechanism raises the F1 score to 93%.<\/jats:p>","DOI":"10.3390\/info16121109","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T14:36:53Z","timestamp":1765895813000},"page":"1109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DLF: A Deep Active Ensemble Learning Framework for Test Case Generation"],"prefix":"10.3390","volume":"16","author":[{"given":"Yaogang","family":"Lu","sequence":"first","affiliation":[{"name":"Beijing New Building Materials Public Limited Company, Beijing 102209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","first-page":"120","article-title":"Importance of software testing in software development life cycle","volume":"11","author":"Khan","year":"2014","journal-title":"Int. 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