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#author("2018-03-22T11:04:39+09:00","","")
[[論文英語単語帳]]
***リンク [#f20ebad6]
#ls2(論文英語単語帳/)
#author("2018-07-15T07:07:14+09:00","","")

*その他 [#pbd1116a]
**アブスト章で役に立ちそう [#ze4c19fb]
***貢献は次の通り [#mf634a02]
- Our core contributions include: i)ARUS, a graph-based representation of API usage scenarios;
ii) HAPI, a statistical, generative model of API usages;
**背景章で役に立ちそう [#k0efd1c5]
***重要な側面の一つ [#p5d07965]
-Performance is one of the key aspects of non-functional qualities, where performance bugs can cause significant performance degradation and lead to poor user experience.
**論文の構成 [#xdd68ec1]
***モチベーション [#lca8c165]
-Section II further explains the motivation for this research initiative.
***アプローチ [#d011ea30]
-Section IV gives an overview of our approach to the mapping exercise. 
***実験 [#rb831638]
***結果 [#l6b480e6]
***考察 [#yec41ae8]
***結論 [#gefbea83]
- We end with some conclusions, as well as a summary of recommendations for those engaged in similar activities, in Section VI. 

** アプローチ章で役に立ちそう[#q2119423]

**実験章で役に立ちそう [#jb2f88a5]

***そのうち， [#lf115900]
-We used only 80% of method sequences
associated with each HAPI for training (of which 12.5% was
randomly selected as the validation set). 

**結果章で役に立ちそう [#sf030a98]
***結果を示す [#o13cab4a]
- Our empirical evaluation suggests that our
approach can learn useful API usage models which can provide
recommendations with higher levels of accuracy than the baseline
n-gram model.
***図 [#f18587cb]
-As seen in the figure
-we present an overview of the defect categorization approaches in the literature.
- Figure 1 gives an overview of the multiple knapsack problem
***表 [#a098792d]
-Table I summarizes the data we collected for our experiments.
-Table III provides detailed results regarding severity.

**関連研究章で役に立ちそう [#x647e8f5]
***近いものとして [#ec5d16b9]
-closest to
--Closest to our work, is the series of work on bug report severity prediction by Menzies and Marcus [16], Lamkanfi et al. [13], [14], and our own previous work [27].
-closer to
--Closer to our work, in as much as some prediction is being made based on observed rules, are the approaches that use the textual similarity between bug descriptions to assign bugs to developers