Posts on concepts and methods related to software development and security that may involve areas such as AI, machine learning and mathematics.

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Jason Chavarría

Five best practices for coding with the help of gen AI

Generative AI tools are an ally for developers to write code efficiently. We share five best practices for developing software securely while using those tools.

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Rafael Ballestas

With symbolic execution

Here's a reflection on the need to represent code before actually feeding it into neural network based encoders, such as code2vec, word2vec, and code2seq.

Book in two languages

Rafael Ballestas

From code to words

Here we talk about Code2seq, which differs in adapting neural machine translation techniques to the task of mapping a snippet of code to a sequence of words.

Target and darts

Rafael Ballestas

Vector representations of code

Here is a tutorial on the usage of code2vec to predict method names, determine the accuracy of the model, and exporting the corresponding vector embeddings.

Arrows vector field

Rafael Ballestas

Vector representations of code

Here we discuss code2vec relation with word2vec and autoencoders to grasp better how feasible it is to represent code as vectors, which is our main interest.

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Rafael Ballestas

Distributed representations of natural language

This post is an overview of word2vec, a method for obtaining vectors that represent natural language in a way that is suitable for machine learning algorithms.

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Rafael Ballestas

Prioritize code auditing via ML

This post is a high-level review of our previous discussion concerning machine learning techniques applied to vulnerability discovery and exploitation.

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Rafael Ballestas

A pipeline to classify vulnerable code

Here is a simple attempt to define a vulnerability classifier using categorical encoding and a basic neural network with a single hidden layer.

Computer showing a graph

Rafael Ballestas

Simple linear regression in scikit

In this post, we begin to tackle why vectors are the most appropriate representation for data as input to machine learning algorithms.

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