GeneticSharp in the Wild: Context-Sensitive Code Completion

I decided to start a series of posts about some outstanding scientific papers and projects using GeneticSharp.

For this second post the chosen one is the Context-Sensitive Code Completion: Improving Predictions with Genetic Algorithms.

Introduction

The main motivation of the author about this paper was: Current methods of training code completion systems can possibly be improved in order to reduce prediction errors. This requires that the amount of information considered in a pattern is optimized. The question raised is then: how should the training of predictive models be focused in order to increase prediction quality?

This thesis will aim to answer the question: to what extent is it possible to improve predictions of existing state-of-the-art code completion systems with a genetic algorithm?

Usage

GeneticSharp is an open-source Genetic Algorithm library for C#, released under the MIT license (Giacomelli). It has an extensible interface that allows for most, if not all, functionality to be implemented from scratch via interfaces or leveraged by extending base classes. Classes and interfaces also use the same terminology that has already been established, which makes the translation from theory to implementation much more clear.

Results

As a result of the new training scheme, the quality of predictions can be increased without losing generalizability. Application of the new training scheme could possibly be applied to any code completion systems that trains a predictive model, making it a candidate for improving existing systems as well as in future research.

Author

Marcus Ording wrote the paper for his degree project in Computer Engineering at KTH Royal Institute of Technology from Stockholm, Sweden.

Further reading

You can access the full paper directly on DiVA Portal.