28 citations · 37 across the 3 of their papers we have counts for
10 papers
Learning to Recognize Actionable Static Code Warnings (is Intrinsically Easy)
Xueqi Yang, Jianfeng Chen, Rahul Yedida +2
Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the "actionable" warnings; i.e.…
Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach
Zhe Yu, Fahmid Morshed Fahid, Huy Tu +1
Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts t…
Understanding Static Code Warnings: an Incremental AI Approach
Xueqi Yang, Zhe Yu, Junjie Wang +1
Knowledge-based systems reason over some knowledge base. Hence, an important issue for such systems is how to acquire the knowledge needed for their inference. This paper assesses…
TERMINATOR: Better Automated UI Test Case Prioritization
Zhe Yu, Fahmid M. Fahid, Tim Menzies +3
Automated UI testing is an important component of the continuous integration process of software development. A modern web-based UI is an amalgam of reports from dozens of microser…
Better Technical Debt Detection via SURVEYing
Fahmid M. Fahid, Zhe Yu, Tim Menzies
Software analytics can be improved by surveying; i.e. rechecking and (possibly) revising the labels offered by prior analysis. Surveying is a time-consuming task and effective surv…
Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)
Huy Tu, Zhe Yu, Tim Menzies
Standard automatic methods for recognizing problematic development commits can be greatly improved via the incremental application of human+artificial expertise. In this approach,…