Omkar2118/datasets huggingface dataset:
EVIL-Encoders

This dataset contains samples to generate Python code for security exploits. In order to make the dataset representative of real exploits, it includes code snippets drawn from exploits from public databases. Differing from general-purpose Python code found in previous datasets, the Python code of real exploits entails low-level operations on byte data for obfuscation purposes (i.e., to encode shellcodes). Therefore, real exploits make extensive use of Python instructions for converting data between different encoders, for performing low-level arithmetic and logical operations, and for bit-level slicing, which cannot be found in the previous general-purpose Python datasets. 
In total, we built a dataset that consists of 1,114 original samples of exploit-tailored Python snippets and their corresponding intent in the English language. These samples include complex and nested instructions, as typical of Python programming. 
In order to perform more realistic training and for a fair evaluation, we left untouched the developers' original code snippets and did not decompose them. We provided English intents to describe nested instructions altogether. 
In order to bootstrap the training process for the NMT model, we include in our dataset both the original, exploit-oriented snippets and snippets from a previous general-purpose Python dataset. This enables the NMT model to generate code that can mix general-purpose and exploit-oriented instructions. Among the several datasets for Python code generation, we choose the Django dataset due to its large size. This corpus contains 14,426 unique pairs of Python statements from the Django Web application framework and their corresponding description in English.
Therefore, our final dataset contains 15,540 unique pairs of Python code snippets alongside their intents in natural language.

Introducing paper: EVIL: Exploiting Software via Natural Language
Failure-Dataset-OpenStack

This failure dataset contains information on the events collected in the OpenStack cloud computing platform during three different campaigns of fault-injection experiments performed with three different workloads.

Introducing paper: Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning