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

Software development often involves systematic edits, similar butnonidentical changes to many code locations, that are error-prone and laboriousfor developers. Mining and learning such systematic edit patterns (SEPs) frompast code changes enable us to detect and repair overlooked buggy code thatrequires systematic edits.  A recent study presented a promising SEP mining technique that is based onprogram dependence graphs (PDGs), while traditional approaches leveragesyntax-based representations. PDG-based SEPs are highly expressive and cancapture more meaningful changes than syntax-based ones. The next challenge totackle is to apply the same code changes as in PDG-based SEPs to other codelocations; detection and repair of overlooked locations that require systematicedits. Existing program transformation techniques cannot well address thischallenge because (1) they expect many structural code similarities that arenot guaranteed in PDG-based SEPs or (2) they work on the basis of PDGs but arelimited to specific domains (e.g., API migrations).  We present in this paper a general-purpose program transformation algorithmfor applying PDG-based SEPs. Our algorithm identifies a small transplantablestructural subtree for each PDG node, thereby adapting code changes fromPDG-based SEPs to other locations. We construct a program repair pipelineSirius that incorporates the algorithm and automates the processes of miningSEPs, detecting overlooked code locations (bugs) that require systematic edits,and repairing them by applying SEPs.  We evaluated the repair performance of Sirius with a corpus of open sourcesoftware consisting of over 80 repositories. Sirius achieved a precision of0.710, recall of 0.565, and F1-score of 0.630, while those of thestate-of-the-art technique were 0.470, 0.141, and 0.216, respectively.

Sirius: Static Program Repair with Dependence Graph-Based Systematic Edit Patterns