WASP Software Engineering course (part)

This repo contains course materials, resources/links, and student assignments for the Software Engineering (SE) part of the WASP "SE and Cloud Computing" course.

Papers on SE for AI/ML

• Software Engineering for AI/ML -- An Annotated Bibliography

Tools (with background reading)

Cheat Sheets

• Git Cheat Sheet
• Docker - Ultimate Cheat Sheet

Version control

• Understanding Version-Control Systems by Eric Raymond
• Atomic Commits by Nathan Rambeck
• Semantic Versioning specification
• Introduction to Semantic Versioning

IDEs

• VS Code

Code autocomplete

• TabNine - code autocomplete for many languages (including Python, Java, Javascript, and more)
  • Code Autocomplete is a VS code extension that access TabNine for code autocomplete

ML-QA Toolkit

• QA-Mapping-for-ML - results of a recent Master thesis at Chalmers
• ML Testing Paper Mapping Tool - online filtering tool for the ML testing papers

Data Validation

• Google Data Linter
• Amazon Dequu
• Python csvvalidator
• CSV Validator

Design By Contract

• Pre/Post-Conditions in Python Decorator Library
• StackOverflow thread on ways to do DBC in Python

Unit Testing

• Effective Python Testing With Pytest
• Unit Testing Your Code with the unittest Module by Corey Schafer

Property-based testing (PBT)

• Hypothesis PBT in Python
• Property based testing — A practical approach in Python with Hypothesis and Pandas

Coding standards and Code Reviews

• Style Guide for Python Code
• Coding Standards and Best Practices for Python Code Quality
• How To Review Someone Else’s Code: Tips and Best Practices

QA/Static analysis

• pylint - static code analysis for Python
• Using pylint to write clean Python code
• pylama code audit tool for Python
• lintR static code analysis for R
• StaticLint.jl - static code analysis for Julia
• WALA static code analysis for Java bytecode and related languages

Static analysis for ML

• mllint - Linter for Machine Learning projects
• pynblint - linter for Python Notebooks, this is the one you will try in assignment 1 in the course
• nblyzer notebook static analysis tool/paper from Microsoft
• MLSmellHound - paper describing adaptation of pylint for ML projects
• Ariadne / WALA extension to WALA (above) for Python and Tensorflow

Testing of DNNs

• Tonella Keynote at ICST 2022: "Misconceptions in Deep Learning Testing"