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Challenging machine learning algorithms in predicting vulnerable JavaScript functions.

, , , , , and . RAISE@ICSE, page 8-14. IEEE / ACM, (2019)

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Developer Focus: Lack of Impact on Maintainability., and . ICCSA (5), volume 10964 of Lecture Notes in Computer Science, page 391-402. Springer, (2018)An AST-based Code Change Representation and its Performance in Just-in-time Vulnerability Prediction., , and . CoRR, (2023)Static Code Analysis Alarms Filtering Reloaded: A New Real-World Dataset and its ML-Based Utilization., and . IEEE Access, (2022)Is JavaScript Call Graph Extraction Solved Yet? A Comparative Study of Static and Dynamic Tools., , , , and . IEEE Access, (2023)Experimental evaluation of kinect and inertial sensors for beating heart tracking., , , and . CURAC, volume 1477 of CEUR Workshop Proceedings, page 9-12. Innsbruck Medical University, (2013)A Data-Mining Based Study of Security Vulnerability Types and Their Mitigation in Different Languages., , , and . ICCSA (4), volume 12252 of Lecture Notes in Computer Science, page 1019-1034. Springer, (2020)Relating Clusterization Measures and Software Quality., , , , , and . CSMR, page 345-348. IEEE Computer Society, (2013)A probabilistic software quality model., , , , and . ICSM, page 243-252. IEEE Computer Society, (2011)Do automatic refactorings improve maintainability? An industrial case study., , , , and . ICSME, page 429-438. IEEE Computer Society, (2015)A Drill-Down Approach for Measuring Maintainability at Source Code Element Level., , , , and . ECEASST, (2013)