The field of predictive modeling and software development is currently experiencing a significant shift towards more robust and context-dependent approaches. Researchers are moving away from universal predictors and towards more nuanced methods that take into account the specific goals, stage, and data used for evaluation. This is evident in the development of new frameworks and techniques that prioritize sustained positive impact, reduce dimensionality, and emphasize key features. Furthermore, innovative methods such as collaborative fuzzing and control flow attestation are being explored to improve software development and security. Noteworthy papers include:

• A quantitative meta-analysis that reveals the most consistently powerful predictors of startup success are foundational attributes such as firm characteristics and investor structure.
• The introduction of the MH-FSF framework, a comprehensive platform for feature selection evaluation that provides implementations of 17 methods and enables systematic evaluation on 10 publicly available Android malware datasets.
• The proposal of RESPEC-CFA, an architectural extension for control flow attestation that reduces control flow log sizes by up to 90.1%.
• The development of PathFuzzing, a method that combines the strengths of fuzzing and symbolic execution to design a worst-case analysis technique.