7 citations · 13 across the 6 of their papers we have counts for
6 papers · 1 filter
Test suite effectiveness metric evaluation: what do we know and what should we do?
Peng Zhang, Yang Wang, Xutong Liu +6
Comparing test suite effectiveness metrics has always been a research hotspot. However, prior studies have different conclusions or even contradict each other for comparing differe…
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
Linghan Meng, Yanhui Li, Lin Chen +4
The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models a…
Mutant reduction evaluation: what is there and what is missing?
Peng Zhang, Yang Wang, Xutong Liu +6
Background. Many mutation reduction strategies, which aim to reduce the number of mutants, have been proposed. Problem. It is important to measure the ability of a mutation reducti…
An extensive empirical study of inconsistent labels in multi-version-project defect data sets
Shiran Liu, Zhaoqiang Guo, Yanhui Li +4
The label quality of defect data sets has a direct influence on the reliability of defect prediction models. In this study, for multi-version-project defect data sets, we propose a…
Prioritizing documentation effort: Can we do better?
Shiran Liu, Zhaoqiang Guo, Yanhui Li +5
Code documentations are essential for software quality assurance, but due to time or economic pressures, code developers are often unable to write documents for all modules in a pr…
MAT: A simple yet strong baseline for identifying self-admitted technical debt
Zhaoqiang Guo, Shiran Liu, Jinping Liu +5
In the process of software evolution, developers often sacrifice the long-term code quality to satisfy the short-term goals due to specific reasons, which is called technical debt.…