Daily Papers

Neonatal death is still a concerning reality for underdeveloped and even some developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births die, according to Macro Trades. To reduce this number, early prediction of endangered babies is crucial. Such prediction enables the opportunity to take ample care of the child and mother so that early child death can be avoided. In this context, machine learning was used to determine whether a newborn baby is at risk. To train the predictive model, historical data of 1.4 million newborns was used. Machine learning and deep learning techniques such as logical regression, K-nearest neighbor, random forest classifier, extreme gradient boosting (XGBoost), convolutional neural network, and long short-term memory (LSTM) were implemented using the dataset to identify the most accurate model for predicting neonatal mortality. Among the machine learning algorithms, XGBoost and random forest classifier achieved the best accuracy with 94%, while among the deep learning models, LSTM delivered the highest accuracy with 99%. Therefore, using LSTM appears to be the most suitable approach to predict whether precautionary measures for a child are necessary.

Large Language Models (LLMs) for code generation evolve rapidly through fine-tuning, merging, or new model releases. However, such updates can introduce regressions, not only in correctness but also in code quality and performance. To address this, we present ReCatcher, a regression testing framework for Python code generation. ReCatcher systematically compares two LLMs, typically a current model and a candidate update, across three dimensions: logical correctness, static code quality, and execution performance. We apply ReCatcher to assess regressions across three update scenarios, fine-tuning, merging, and model release, using CodeLlama, DeepSeek-Coder, and GPT-4o. Our evaluation shows that fine-tuning with cross-language datasets increases syntax errors by up to 12%. Merging with general-purpose models like Llama2 leads to regressions in correctness by up to 18%. GPT-4o introduces regressions of up to 50% in handling missing imports compared to GPT-3.5-turbo, while GPT-4o-mini suffers up to 80% performance degradation in execution time versus GPT-4o. Overall, logical correctness, performance, and error handling (e.g., syntax errors and missing imports) are the most regression-prone areas. Comparing ReCatcher with baseline solutions, it presents better and consistent accuracy across logical and performance aspects. ReCatcher highlights the importance of systematic regression evaluation before adopting new models, while assisting researchers and practitioners in making more informed update decisions.