Static Analysis Complements Machine Learning: A Type Inference Use Case

feng, L.

Static Analysis Complements Machine Learning: A Type Inference Use Case

Master thesis (2023)

Authors

L. feng Electrical Engineering, Mathematics and Computer Science

Contributors

G. Gousios Software Technology (mentor)

C.B. Poulsen Programming Languages - (graduation committee member)

Faculty

Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science

Machine Learning (ML) Type Inference Static analysis

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Published Date

28-08-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Type inference plays a pivotal role in modern software development as it aids in understanding code, detecting errors, and facilitating code completion. Two main approaches, static analysis, and machine learning, contribute to this process. Each approach has its own benefits and limitations. This thesis investigates the potential of combining static analysis techniques and machine learning (ML) approaches to enhance type inference capabilities.

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