Update README.md

README.md

@@ -136,7 +136,7 @@ The features we extract for each repository are illustrated in the example below
 - **retrieval_date**: date when the repo was scraped from GitHub
 
 We start by retrieving repositories with more than **900** stars using **two-month tumbling windows**. If we hit the **1000** repository limit per window (for a personal GitHub account), we shorten the
-search space to a **one-month window** and restart the iteration. Otherwise, the window advances by two months. Once the 2001-2024 timeframe is covered, we reduce the star search space: between **900** and **100** stars, we decrease the interval by **50** (e.g. search between [900, 850]), between **100** and **10** stars, we decrease the interval by **10**, and for the last **10** stars, we decrease by **1**. Figure 1 showcases the distribution of repositories with up to **500** stars. Since most repositories fall within the **0-100 star range**, using the **creation date** and **star count** filters helps us avoid API limits and scrape more data by narrowing the search space. 
+search space to a **one-month window** and restart the iteration. Otherwise, the window advances by two months. Once the timeframe until April 2024 is covered, we reduce the star search space: between **900** and **100** stars, we decrease the interval by **50** (e.g. search between [900, 850]), between **100** and **10** stars, we decrease the interval by **10**, and for the last **10** stars, we decrease by **1**. Figure 1 showcases the distribution of repositories with up to **500** stars. Since most repositories fall within the **0-100 star range**, using the **creation date** and **star count** filters helps us avoid API limits and scrape more data by narrowing the search space. 
 Although the creation date window can be reduced even further (week or day level), a one-month window was enough for our needs. After retrieving the repositories, we extract all the files with a
 *.java* extension. 
 
@@ -216,7 +216,7 @@ Thus, given the increased **complexity** and **size** of Java files, we consider
 Furthermore, **k = 9** was shown to be a safe choice for [large research articles](http://infolab.stanford.edu/~ullman/mmds/book.pdf), however, for our needs, 7-shingles strike a balance between accuracy and
 computational efficiency, crucial for handling the **Java-Stack v2’s size** of over **222 M** files. This choice provides better computational efficiency by reducing the number of comparisons while maintaining a manageable shingle space.
 Lastly, we use a **Jaccard similarity threshold** of **0.7**, which proved to be efficient for both [SantaCoder](https://arxiv.org/abs/2301.03988) and [StarCoder](https://arxiv.org/abs/2305.06161) models. Such a high threshold
-reduces false positives, leading to fewer unnecessary comparisons and lower computational overhead. 
+reduces false positives, leading to fewer unnecessary comparisons and lower computational overhead. Moreover, this standard threshold value has been shown to be [robust for duplicate detection](https://dl.acm.org/doi/10.1145/3359591.3359735). 
 
 Instead of removing near-duplicates, we introduce a new feature to our dataset, called *near_dups_stkv2_idx*. This feature is a list of IDs of the near-duplicate files from the Java-Stack v2 corresponding to the current file in our dataset.
 The table below shows the number of files removed by each preprocessing method and the final number of files we are left with in the end (excluding near-duplicates).