English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Contextual information plays a vital role for software developers whenunderstanding and fixing a bug. Consequently, deep learning-based programrepair techniques leverage context for bug fixes. However, existing techniquestreat context in an arbitrary manner, by extracting code in close proximity ofthe buggy statement within the enclosing file, class, or method, without anyanalysis to find actual relations with the bug. To reduce noise, they use apredefined maximum limit on the number of tokens to be used as context. Wepresent a program slicing-based approach, in which instead of arbitrarilyincluding code as context, we analyze statements that have a control or datadependency on the buggy statement. We propose a novel concept called dualslicing, which leverages the context of both buggy and fixed versions of thecode to capture relevant repair ingredients. We present our technique and toolcalled Katana, the first to apply slicing-based context for a program repairtask. The results show Katana effectively preserves sufficient information fora model to choose contextual information while reducing noise. We compareagainst four recent state-of-the-art context-aware program repair techniques.Our results show Katana fixes between 1.5 to 3.7 times more bugs than existingtechniques.

Katana: Dual Slicing-Based Context for Learning Bug Fixes