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給了一個(gè)月的修訂時(shí)間,我10天就改好了。我要不要等到一個(gè)月后再投? 已有6人參與
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審稿意見: Reviewer #1: I understand that AL-based approach needs both "initial training set" and "the newly added samples" in order to select most representative samples. However, there are two questions. First, what do you mean representative samples? Does it mean a set of samples which covers both valid and invalid links? Second, it seems that in the Algorithm 1, active learning relies on the termination condition to ensure the representativeness of the samples. If I am correct, the termination condition used in the paper (i.e., the number of labeled samples reaches a preset value) does not make any sense. A better termination condition could be the labeled samples shall contains at least 1 valid link and 1 invalid link. Reviewer #2: Tracking the relation between artifacts in software project is important. Generally, it is human intensive task to construct the traceability links. Traditional information retrieve techniques has been employed to automatic analyze and recover traceability links. Even machine learning approaches as adopted to train an effective predictive model for traceability link recovery. It requires humans to label traceability links. This paper presents a TLR approach based on active learning. Evaluation experiments were conducted on seven commonly used traceability datasets. It was compared with an IR-based approach and a current machine learning approach. The experiment shows that AL-based approach outperforms the other two approaches in terms of F-score. Concerns 1、 Page1, "(hereafter called AL-based approach)" is repeated in the abstract and introduction. 2、 Page 1, section 1, left column, last line, "traceability" means "traceability relationships" or "traceability links"? 3、 Page 2, left column, "TSL-based approach is that how to select traceability links for labeling to generate traceability information."=》that 4、 Page 3, Section 3, Step1: (1)" randomly selecting a small number of samples for labeling to initialize Dt", =>"randomly labeling a small number of samples to initialize Dt " 5、 Page 3, Section 3, Step1: (3) "selecting an unlabeled sample from the unlabeled sample set based on sample selection strategy and requesting experts to label the sample"=> The authors need to define the D and Dl here. Regarding to the context, the Dt and Dl seem equivalent, why use different symbol? 6、 Page 3, Section 3, Step 4, This paper chose Random Forest as the classification algorithm. However, the authors only claimed that "The reason for choosing Random Forest is because it has been shown to be accurate and robust". It would be better to explain why random forest is more suitable for the task. 7、 Page 4, Algorithm1 needs to be reconstructed. It would be much better to define input and output, as well as all the variables used in the algorithm. 8、 Section 4. It would be much better to move Experimental metric at the beginning of section 4. Authors use the F-score before its definition. 9、 The format of refences should be standardized, especially, the names and abbreviations of journals and conferences. |
鐵桿木蟲 (知名作家)
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完全沒有必要,覺得改好了就返回吧 發(fā)自小木蟲IOS客戶端 |
木蟲之王 (文學(xué)泰斗)
鐵桿木蟲 (職業(yè)作家)
劍橋第一人

新蟲 (著名寫手)
新蟲 (正式寫手)
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我投的JAC,大修,給了一個(gè)月時(shí)間,兩個(gè)審稿人一共給了12個(gè)問題,我和導(dǎo)師12天認(rèn)真改完,修改稿都2500字,較為詳細(xì),但也不啰嗦。又檢查了兩遍,14天返回的。我大師兄說返回的太早了,一般20來(lái)天返回更合適。小導(dǎo)師說沒必要,改好返稿就行,不知道改完得等多久一般 發(fā)自小木蟲IOS客戶端 |
金蟲 (小有名氣)
新蟲 (文壇精英)
鐵桿木蟲 (職業(yè)作家)
劍橋第一人
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不會(huì)的,但是你一定要把審稿人所有的問題全部回答完。我當(dāng)時(shí)投的MSEA,給的小修,所以很好改,沒必要拖很久。 發(fā)自小木蟲Android客戶端 |

新蟲 (正式寫手)
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改好了,等個(gè)一兩天再看看有沒有問題,返回就好。到了編輯手里,處理慢的話,也要一周才能發(fā)給審稿人 發(fā)自小木蟲Android客戶端 |
鐵桿木蟲 (職業(yè)作家)
劍橋第一人

新蟲 (正式寫手)
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看不到審了多久,under review 大概2周收到的審稿意見,現(xiàn)在返稿6天了,一般提交修改稿會(huì)多久收到結(jié)果,謝謝 發(fā)自小木蟲IOS客戶端 |
新蟲 (正式寫手)
捐助貴賓 (著名寫手)
新蟲 (著名寫手)
新蟲 (正式寫手)
新蟲 (正式寫手)
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