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您的检索式：作者名="Wanwangying MA"
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| 1 | A study on the changes of dynamic feature code when fixing bugs: towards the benefits and costs of Python dynamic features显示文摘Dynamic features in programming languages support the modification of the execution status at runtime, which is often considered helpful in rapid development and prototyping. However, it was also reported that some dynamic feature code tends to be change-prone or error-prone. We present the first study that analyzes the changes of dynamic feature code and the roles of dynamic features in bug-fix activities for the Python language. We used an AST-based differencing tool to capture fine-grained source code changes from 17926 bug-fix commits in 17 Python projects. Using this data, we conducted an empirical study on the changes of dynamic feature code when fixing bugs in Python. First, we investigated the characteristics of dynamic feature code changes, by comparing the changes between dynamic feature code and non-dynamic feature code when fixing bugs, and comparing dynamic feature changes between bug-fix and non-bugfix activities. Second, we explored 226 bug-fix commits to investigate the motivation and behaviors of dynamic feature changes when fixing bugs. The study results reveal that(1) the changes of dynamic feature code are significantly related to bug-fix activities rather than non-bugfix activities;(2) compared with non-dynamic feature code, dynamic feature code is inserted or updated more frequently when fixing bugs;(3) developers often insert dynamic feature code as type checks or attribute checks to fix type errors and attribute errors;(4) the misuse of dynamic features introduces bugs in dynamic feature code, and the bugs are often fixed by adding a check or adding an exception handling. As a benefit of this paper, we gain insights into the manner in which developers and researchers handle the changes of dynamic feature code when fixing bugs. | Zhifei CHEN Wanwangying MA Wei LIN Lin CHEN Yanhui LI Baowen XU | 2018 | Science China(Information Sciences)2018，61，1： | 4 |
| 2 | Empirical analysis of network measures for predicting high severity software faults显示文摘Network measures are useful for predicting fault-prone modules. However, existing work has not distinguished faults according to their severity. In practice, high severity faults cause serious problems and require further attention. In this study, we explored the utility of network measures in high severity faultproneness prediction. We constructed software source code networks for four open-source projects by extracting the dependencies between modules. We then used univariate logistic regression to investigate the associations between each network measure and fault-proneness at a high severity level. We built multivariate prediction models to examine their explanatory ability for fault-proneness, as well as evaluated their predictive effectiveness compared to code metrics under forward-release and cross-project predictions. The results revealed the following:(1) most network measures are significantly related to high severity fault-proneness;(2) network measures generally have comparable explanatory abilities and predictive powers to those of code metrics; and(3) network measures are very unstable for cross-project predictions. These results indicate that network measures are of practical value in high severity fault-proneness prediction. | Lin CHEN Wanwangying MA Yuming ZHOU Lei XU Ziyuan WANG Zhifei CHEN Baowen XU | 2016 | Science China Earth Sciences2016，59，12： | 3 |
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