Towards Automatically Repairing Errors in Pythonand
Testing FAISS indexing on DiffSearch, and one bacher thesis on
Improving the Recall of Searching for Code Changes.
We extract 1.4 million type annotation changes from 9,655 Python repositories. Our results show that type annotations are clearly gaining traction, yet the large majority of code elements that could be annotated currently remains unannotated. We see a huge potential for techniques that automate the process of adding types into an existing code base, such as neural type prediction models. Finally, many developers seem to not regularly check their code for statically detectable type errors, or if they do, commit the code despite such errors.
We present a scalable and precise search engine for code changes. Given a query, the approach retrieves within seconds relevant examples from million code changes. Our query language extends the underlying programming language, providing an intuitive way of formulating queries to search for code changes. DiffSearch encodes both queries and code changes into a common feature space, enabling efficient retrieval of candidate search results. DiffSearch guarantees that every returned search result fits the query.
This article provides a comprehensive overview of 30 years of research on code search. Given the huge amounts of existing code, searching for specific code examples is a common activity during software development. We discuss what kinds of queries code search engines support, and give an overview of the main components used to retrieve suitable code examples. In particular, the article discusses techniques to pre-process and expand queries, approaches toward indexing and retrieving code, and ways of pruning and ranking search results.
The first part of this study focused on building the protein dataset using a simulation tool and performing feature extraction using novel geometrical descriptors. The second part involved in a classification of tubulin isotypes and a comparison of tubulin with the FtsZ protein.
This study proposes a novel quality function deployment (QFD) design methodology based on customers emotions conveyed by facial expressions. The current advances in pattern recognition related to face recognition techniques have fostered the cross-fertilization and pollination between this context and other fields, such as product design and human-computer interaction.