Including many features present in Canon’s higher end S and G collection cameras which provide customers flexibility and inventive management, it stands for excellent image quality and ease of use. Since the selected data may greatly impact our results, we dedicate this section to describe the data collection process in detail. We include this feature set since more code changes may increase the chance of conflicts. The high impact of the number of simultaneously changed files can be intuitively explained since more in-parallel changes increase the likelihood of conflicts, and the chance of conflicts is zero if there are no simultaneously changed files. It needs 5 values to represent number of added, deleted, modified, copied, and renamed files. Merge conflict data gathered from Git history is highly imbalanced; specifically, the number of merge scenarios without conflicts is much higher than merge scenarios with conflicts. For a given merge scenario, any of the binary classifiers we compare predicts either a conflict or a not conflict (i.e., a safe merge). We show the number of repositories, merge scenarios, conflicting merge scenarios, and the conflict rate of each programming language in detail in Table II.
After choosing the target repositories, we analyze their latest 1,00010001,0001 , 000 merge scenarios. For practical limitations with respect to computational resources for replaying thousands of merge scenarios from that many repositories, we only consider repositories whose size is less than 1111 GB and focus on the latest 1,00010001,0001 , 000 merge scenarios in each repository. Considering the three criteria mentioned above, we sort the well-engineered repositories in each programming language separately based on the number of stars. Quality: Even though the number of stars represents some measure of quality, not all popular repositories are suitable for our study. The list of the selected repositories we use in our experiments is available on our artifact page. Our artifact page contains the exact git log commands we use. For all the feature sets listed in the table, we use the git log command, with different parameters depending on the feature set, to extract their values. Although we report the correlation and importance of feature sets for different programming languages separately, it is important to note that we do not expect to see significant differences between the feature sets in different programming languages since our feature sets are language-agnostic. Therefore, we report all our performance measures for both the conflicting (C) and safe (S) classes as follows. Therefore, having a second opinion during an interview is paramount to choosing the best candidate. This article has been gen er ated wi th the help of GSA Conte nt Generator D em ov er sion .
Nor having to download the software cracks the protection was designed to prevent, purely so you can actually run the software you’ve paid for. That way, we can determine if having the other features improves things, or is simply an added cost with no benefit. Experiment toggles: Experiment toggles are used to perform experimentation on the software, such as is done by Microsoft microsoftexperiment kohavi2009controlled , to evaluate new features changes and their influence on user-observable behavior. When passenger cars displaced equestrian travel and the myriad occupations that supported it in the 1920s, the roadside motel and fast-food industries rose up to serve the “motoring public.” How will changes in mobility, for example, enable and shape changes in distribution and consumption? Rebasing is another strategy for integrating changes from different branches. Strategy in the game reflects history. We employ the default one (recursive merging strategy) since developers typically do not change the default configuration of Git merge. Another change this preview brings is in how apps open links. Example apps that can be created include fleet management, route planning, traffic management and geospatial analysis. Once your shareware has been created. This article was done with GSA C ontent Generator Dem oversion!
It is important to note that accuracy is not a good performance measure for imbalanced data since the potential influence of misclassification of conflicting merges would be much lower than safe merges. The candidate values for each of these hyper-parameters are selected based on the typical values explored for this size of data. The main hyper-parameters of decision trees and random forests classifiers are (1) the minimum samples in leaves, (2) the minimum sample split, (3) the maximum depth, and (4) the total number of estimators (just for random forest). We use a set of candidate values for the hyper-parameters we use for our classifiers. The other hyper-parameters also balance the complexity of the models. However, we cannot guarantee that we found the globally optimal values for our hyper-parameters. Due to the importance of these hyper-parameters, we use grid-search with 10101010-fold cross-validation to find the right hyper-parameters to use. We find that a Random Forest classifier based on light-weight Git features can successfully predict conflicts for different programming languages. Similar to the correlation-based analysis, we find that the importance of feature sets is relatively similar for all programming languages. A decision tree aims to find a single feature set in each level based on which it can classify the data in the most optimized way.
Perhaps the most lucrative way to make money online is by collecting virtual goods that have real-world value. We, thus, need to ensure that the selected repositories are of high quality and reflect real-world development practices. Indeed, the emphasis has shifted from pure model development to real-world production scenarios that are concerned with issues such as inference performance, scaling, load balancing, training time, reproducibility, and visibility. Therefore, if you have several people browsing through pictures at one time, they can each drag, zoom and turn photos at the same time without waiting for each other. Over time, these devices would become more powerful and yet lighter and less cumbersome. We will generally go over some of the main important benefits. Moreover, to avoid analyzing the same merge scenario multiple times, we only analyze the main repositories and eliminate the forked versions. While we do not use the same exact features from the previous work by Leßenich et al. Note that this is the same classifier used to determine importance in RQ1. 1: The first baseline we compare to is a “dummy” classifier that randomly labels the data. 1, which is a “dummy” classifier that randomly labels the data by considering the imbalance rate. 1, all features seem to have very low importance for the classifier.