![[PukiWiki]](image/wadanyan.png "[PukiWiki]")
#author("2018-08-30T03:38:00+09:00","","") #author("2018-08-30T03:38:50+09:00","","") [[論文英語単語帳/表現集/結果章]] ***リンク [#f20ebad6] #ls2(論文英語単語帳/) *** 表現集[#s089e92b] #ls2(論文英語単語帳/表現集/) *を実施した結果 [#x384dbf7] **検定 [#g0cb9f33] -All results are statistically significant with the Wilcoxon rank-sum test with the Benjamini-Hochberg correction. *があった [#s6777204] **有意差がある [#f4331c17] -For our metrics concerning relationships, we found significant differences for three metrics in both analyses: the number of pull requests, comments on other people’s pull requests, and the days since first interactions. All of these metrics have positive effects in both analyses. -The number of issue comments was significant in Analysis 2 only. - we also included in each table the pairwise difference for each metric. That is, we subtracted the quantities of nonjoiners’ metrics from the quantities of joiners. A positive result indicates that the joiner had more activity in that dimension than the nonjoiner - The highest number of such agreement occurred for group A in round 1, while the same group obtained the lowest agreement in round 2, statistically significantly lower than in three of the other cases (except for the pilot study). -We observed that relative activity in Firefox OS is significantly lower during weekends than for Firefox. **エフェクトサイズ [#k6bc5942] -While we both observe a statistically significant difference between projects for both weekend and night work, the effect size of the weekend work is large (Mann–Whitney U test p-value < 0.001, Cohen’s d = 0.94, Cliff’s δ = 0.77) and neglible for night work (Mann–Whitney U test p-value < 0.01, Cohen’s d = 0.06, Cliff’s δ = 0.06). **影響があった [#b5de91a2] ***have impact [#e24e08d8] -Patch size has impact on automatic program repair. **関係している [#n4caef75] -Project specific change adaption indicates those changes that are dependent on project structure, file path etc. **その他 [#g50ca028] -We preferred medians to means because they were more insensitive to the influence of outliers in our data. **平均的に．．． [#hde472e8] -We include the descriptive statistics as well, first, in order to characterize our data more concretely, and second, to point out that joiners are, on the surface, more active in general across every kind of activity within the software communities. -we report the medians and interquartile ranges (IQRs, or the 25%th and 75%th percentiles) of these differences in the rightmost columns of each table. - Furthermore, the quantities that characterized joiners tended to be higher, or at least not lower, to their nonjoining peers in every respect. *予測 [#u4c28bac] **精度が [#a7fbfb76] -The Bernoulli Naive Bayes models achieved its highest accuracy of 20.52% with N-Grams of ranges 1, 2, and 3. Its micro-average ROC AUC was 0.60, as shown in Table 1. AUC’s for each target class, or target command, ranged from 0.5 to 0.96, with a mean of 0.55 and standard deviation of 0.10. -The Multinomial Naive Bayes models achieved its highest accuracy of 20.14% with N-Grams of ranges 1, 2, and 3. Its micro-average ROC AUC was 0.59, as shown in Table 1. AUC’s for each target class, or target command, ranged from 0.5 to 0.97, with a mean of 0.61 and standard deviation of 0.14. -The Logistic Regression model achieved its highest accuracy of 35.87% with N-Grams of ranges 1, 2. Its micro-average ROC AUC was 0.67, as shown in Table 1. AUC’s for each target class, or target command, ranged from 0.50 to 0.95, with a mean of 0.63 and standard deviation +/- 0.12. **回帰係数などが＋orーである [#y411c19e] -For metrics of the individual developer, the number of commits to personal projects and personal followers had a significantly positive impact on joining. -For team metrics, the number of projects, pull requests, and overall number of contributors had a positive impact on joining, whereas the number of people watching or forking the team had a negative impact on joining and overall number of commits. *その他 [#g50ca028] **で比べた理由 [#s482ed79] -We preferred medians to means because they were more insensitive to the influence of outliers in our data.
