Learning to Reuse: Adaptive Model Learning for Evolving Systems
Learning to Reuse: Adaptive Model Learning for Evolving Systems
复制标题
学习重用：进化系统的自适应模型学习
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Simão
中科院分区：
文献类型：
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作者：
C. Damasceno;M. Mousavi;A. Simão
软件系统在其生命周期中经历了几次更改，因此，它们的模型可能会过时。为了解决这个问题，我们提出了一种高效的自适应学习算法，称为\(\mathtt{Partial\Text{-}Dynamic~L^*_M})(\(\mathtt{\Partial L^*_M}\))，它通过动态地探索观察表来丢弃多余的前缀和过时的后缀，从而改进了现有的学习算法。使用18个版本的OpenSSL工具包，我们将我们提出的算法与三种自适应算法进行了比较。对于文献中已有的算法，我们的实验表明，成员查询的数量与版本之间的时间距离之间存在很强的正相关性；对于我们的算法，我们发现成员查询与时间距离之间存在弱的正相关，成员查询的数量也明显减少。这些发现表明，与最先进的算法相比，我们的算法对软件进化的敏感度较低，并且比现有的自适应学习方法更有效。
Software systems undergo several changes along their life-cycle and hence, their models may become outdated. To tackle this issue, we propose an efficient algorithm for adaptive learning, called \(\mathtt {partial\text {-}Dynamic~L^*_M}\) (\(\mathtt {\partial L^*_M}\)) that improves upon the state of the art by exploring observation tables on-the-fly to discard redundant prefixes and deprecated suffixes. Using 18 versions of the OpenSSL toolkit, we compare our proposed algorithm along with three adaptive algorithms. For the existing algorithms in the literature, our experiments indicate a strong positive correlation between number of membership queries and temporal distance between versions and; for our algorithm, we found a weak positive correlation between membership queries and temporal distance, as well, a significantly lower number of membership queries. These findings indicate that, compared to the state-of-the-art algorithms, our \(\mathtt {\partial L^*_M}\) algorithm is less sensitive to software evolution and more efficient than the current approaches for adaptive learning.
影响因子：
16.6
作者：
Thuem, Thomas;Apel, Sven;Saake, Gunter
通讯作者：
Saake, Gunter