Automatic test case generation for dynamically typed languages such as JavaScript is significantly hindered by the absence of explicit type information, which expands the search space for search-based testing and reduces its effectiveness. While prior probabilistic and neural type inference methods address this, they struggle with complex user-defined types, higher-order functions, and external package dependencies. This paper presents and evaluates three LLM-based approaches for type inference in JavaScript. The primary contribution is a Retrieval-Augmented Generation (RAG) approach that constructs a vector database of semantically rich code embeddings. These embeddings include ASTs, program slices, and code annotations. This enables efficient, project-wide context retrieval paired with Chain-of-Thought prompting. In a large-scale empirical evaluation against the SynTest framework, the RAG approach achieves a 29% average accuracy improvement over non-RAG LLM approaches, an 85% reduction in computation time, and a 63% accuracy improvement over probabilistic inference for deep, user-defined types. For primitive types, probabilistic methods remain competitive. These findings motivate future hybrid strategies combining probabilistic and LLM-based inference.

https://doi.org/10.5281/zenodo.19496755 Repository link
Replication package of "Retrieval First: LLM- Assisted Type Inference for Automatic Test Case Generation in JavaScript"

The conducted research explores fitting algorithms for learning curves. Learning curves describe how the performance of a machine learning model changes with the size of the training input. Therefore, fitting these learning curves and extrapolating them can help determine the required data set size for any desired performance.

The paper specifically explores the Learning Curve Database (LCDB) and investigates alternative fitting algorithms to the employed Levenberg-Marquardt (LM). These algorithms are Gradient Descent and BFGS, and the paper aims to determine whether they are more suitable for fitting learning curves than LM.

The algorithms were implemented, both in their default and optimised states, and the results were compared to LM. The results measured mean-squared error (MSE), L1 Loss, individual parametric model performance, and computation time.

The findings showed that Gradient Descent is not a suitable alternative to LM; however, BFGS proved to be competitive, as it is practically identical in performance while being significantly faster than LM. The results answered the proposed aim of the paper and generated new questions that need answering.

Further exploration of the BFGS algorithm and its application on learning curve fitting is recommended. Comparisons between the MSE distribution of LM and BFGS can be further explored, as well as comparisons on new parametric models, learners, and datasets.