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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Software archives contain historical information about the development process of a software system. Using Association rule discovery, the development patterns can be extracted from these archives. This information is useful to support software modification activities, as indicated to software developers which modules are usually modified together during software maintenance or evolution. All previous works focused on mining associations with classical interestingness measure, support-confidence, where some disadvantage existed. The new Interestingness Measure, named as support-new confidence, was proposed by Liu et al. to improve the classical method. In this research, we present the comparison of the efficiency of applying association rule discovery on software archive using classical model and Liu et al.'s model. The experiments were conducted on software archive of KMyMoney software, an open source financial software project. The results show that the efficiency of the rules obtained in new model is higher than the rules obtained in classical model in navigation scenario.

international journal of machine learning and computing

A Comparison of the Efficiency of Applying Association Rule Discovery on Software Archive using Support – Confidence Model and Support – New Confidence Model