Can Automated Impact Analysis Techniques Help Predict Decaying Modules?
Can Automated Impact Analysis Techniques Help Predict Decaying Modules?
复制标题
自动影响分析技术可以帮助预测腐烂的模块吗？
DOI：
10.1109/icsme.2019.00088
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Motoshi Saeki
中科院分区：
文献类型：
--
作者：
Natthawute Sae-Lim;Shinpei Hayashi;Motoshi Saeki
腐朽模块是指质量越来越差，将来可能会发臭的模块。这个概念的提出是为了缓解开发人员无法跟踪代码气味的进展并防止它们发生的问题。为了支持开发人员在主动重构过程中防止代码异味，提出了一种预测方法来检测可能在下一个里程碑中成为衰减模块的模块。我们之前的研究表明，开发人员将修改模块作为开发人员上下文的估计，可以用来显着提高预测模型的性能。然而，它需要对变更位置有充分了解的开发人员手动将这些信息指定给系统。为此，在本研究中，我们探索了使用自动化影响分析技术来评估开发人员的环境。这些技术将使开发人员能够提高衰减模块预测模型的性能，而不需要完善的知识或手动输入系统。此外，我们还研究了影响分析技术的准确性与其对改进衰减模块预测的影响之间的关系，以及未来应该探索的方向。
A decaying module refers to a module whose quality is getting worse and is likely to become smelly in the future. The concept has been proposed to mitigate the problem that developers cannot track the progression of code smells and prevent them from occurring. To support developers in proactive refactoring process to prevent code smells, a prediction approach has been proposed to detect modules that are likely to become decaying modules in the next milestone. Our prior study has shown that modules that developers will modify as an estimation of developers' context can be used to improve the performance of the prediction model significantly. Nevertheless, it requires the developer who has perfect knowledge of locations of changes to manually specify such information to the system. To this end, in this study, we explore the use of automated impact analysis techniques to estimate the developers' context. Such techniques will enable developers to improve the performance of the decaying module prediction model without the need of perfect knowledge or manual input to the system. Furthermore, we conduct a study on the relationship between the accuracy of an impact analysis technique and its effect on improving decaying module prediction, as well as the future direction that should be explored.
确定代码债务的优先级：代码气味强度指数
DOI：
10.1109/mtd.2015.7332620
发表时间：
2015
期刊：
2015 IEEE 7th International Workshop on Managing Technical Debt (MTD)
影响因子：
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作者：
F. Fontana;Vincenzo Ferme;M. Zanoni;Riccardo Roveda
通讯作者：
Riccardo Roveda
DOI：
--
发表时间：
2013
期刊：
影响因子：
--
作者：
Takashi Ishio;Shinpei Hayashi;Hiroshi Kazato;Tsuyoshi Oshima
通讯作者：
Tsuyoshi Oshima