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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The lack of comprehensive sources of accurate vulnerability data represents acritical obstacle to studying and understanding software vulnerabilities (andtheir corrections). In this paper, we present an approach that combinesheuristics stemming from practical experience and machine-learning (ML) -specifically, natural language processing (NLP) - to address this problem. Ourmethod consists of three phases. First, an advisory record containing keyinformation about a vulnerability is extracted from an advisory (expressed innatural language). Second, using heuristics, a subset of candidate fix commitsis obtained from the source code repository of the affected project byfiltering out commits that are known to be irrelevant for the task at hand.Finally, for each such candidate commit, our method builds a numerical featurevector reflecting the characteristics of the commit that are relevant topredicting its match with the advisory at hand. The feature vectors are thenexploited for building a final ranked list of candidate fixing commits. Thescore attributed by the ML model to each feature is kept visible to the users,allowing them to interpret the predictions.  We evaluated our approach using a prototype implementation named FixFinder ona manually curated data set that comprises 2,391 known fix commitscorresponding to 1,248 public vulnerability advisories. When considering thetop-10 commits in the ranked results, our implementation could successfullyidentify at least one fix commit for up to 84.03% of the vulnerabilities (witha fix commit on the first position for 65.06% of the vulnerabilities). Inconclusion, our method reduces considerably the effort needed to search OSSrepositories for the commits that fix known vulnerabilities.

Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories