The "Awesome Empirical Software Engineering" project is a comprehensive collection of resources dedicated to the field of empirical software engineering, which emphasizes evidence-based research on software systems and practices. This list encompasses a variety of categories including academic papers, case studies, tools for data collection and analysis, and guidelines for conducting empirical studies. It serves as a valuable resource for researchers, practitioners, and students interested in understanding and applying empirical methods to improve software development processes. By exploring these resources, users can gain insights into best practices and contribute to the advancement of software engineering knowledge.
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The "Awesome Open Source Society University" project is a curated collection of resources aimed at individuals pursuing self-directed learning through open-source educational materials. This list encompasses a variety of categories including online courses, textbooks, lecture notes, and community-driven projects that promote open education. It is particularly beneficial for self-learners, educators, and anyone interested in alternative education models, providing them with the tools and knowledge to explore diverse subjects at their own pace. Users can discover innovative learning paths and connect with a community that values open knowledge sharing.
The "Awesome Machine Learning" project is a comprehensive collection of resources focused on the field of machine learning, which involves algorithms and statistical models that enable computers to perform tasks without explicit instructions. This list encompasses a wide range of categories, including libraries, frameworks, datasets, tutorials, research papers, and community resources. It is designed to benefit everyone from beginners looking to understand the basics to experienced practitioners seeking advanced techniques and tools. By exploring this collection, users can enhance their knowledge and skills in machine learning, paving the way for innovative applications and solutions in various domains.
The "Awesome University Courses" project is a curated resource list that compiles university-level courses from various disciplines available online. This list covers a wide range of subjects including computer science, mathematics, humanities, and social sciences, featuring courses from renowned institutions and platforms. It benefits students, educators, and lifelong learners by providing access to high-quality educational content that can enhance knowledge and skills. Whether you're looking to deepen your understanding of a specific topic or explore new fields, this collection offers a wealth of opportunities for academic growth and personal development.
The "Awesome Data Science" project is a curated collection of resources for individuals interested in the field of data science, which encompasses the extraction of insights and knowledge from structured and unstructured data. This list includes a variety of resources such as libraries, frameworks, datasets, tutorials, courses, and tools that are essential for data analysis, machine learning, and statistical modeling. Whether you are a beginner looking to learn the basics or an experienced data scientist seeking advanced techniques, this list provides valuable information to enhance your skills and projects. Dive into this collection to discover tools and knowledge that can help you excel in your data science journey.
Code and data repository for reproducing examples from 'Evidence-based Software Engineering' book using publicly available data.
A database of reproducible real-world Java bugs and a framework for controlled software engineering experiments.
A Git repository containing the complete historical evolution of Unix from 1970 to the present day.
A Kotlin library for extracting path-based code representations and ASTs from multiple languages to prepare code for machine learning models.
A tool for mining commits from Git repositories to automatically extract code change pattern instances and features using AST analysis.
A Java code quality assessment tool that detects design/implementation smells and computes object-oriented metrics.
A command-line tool to fetch and gather data from software repositories and development platforms using modular backends.
A Python framework for mining and analyzing Git repositories, extracting commits, developers, files, diffs, and source code.
A command-line tool that analyzes C source code files and outputs dozens of code quality metrics related to size, complexity, style, and preprocessor usage.
A tool that calculates a GitHub repository's quality score based on engineering best practices using metadata and source analysis.
A library that detects refactorings and generates AST diffs for Java, Python, and Kotlin code changes.