Mining Docker Posts in SoF

## docker.csv File containing data for metrics to determine popularity, difficulty and correlation (*RQ2*) ## kendall_another.py Python code to determine pvalue and correlation among popular and difficult topics (*RQ2*) ## Docker_Topics_Keyword_Category.csv List of all 30 topics with their keyword, topic name, topic number and categories (*RQ1*) ## Docker_Topics_Popularity.csv Ordering of all 30 topics according to their popularity based on average view, favorite and score count (*RQ2*) ## Docker_Topics_Difficulty.csv Ordering of all 30 topics according to their difficulty based on average percentage of questions without accepted answer and median time (minutes) to receive an accepted answer (*RQ2*) ## correlation.png Graph to visualize topic popularity and difficulty (*RQ2*) ## pvalue_correlation.csv Results from kendall_another.py (*RQ2*) ## expert_comparison_web_ml_docker_general.csv List of web, machine learning and sample baseline development experts along with their expertise value (*RQ3*) ## experts_docker.csv List of docker development experts along with their expertise value (*RQ3*) ## expert_comparison_web_docker.csv Comparison of experts in web and docker domain (*RQ3*) ## Expert Comparison all.png Graph for comparing expert in docker, machine learning, baseline sample and web development (*RQ3*) ## man_kendall.py Python code to generate trends of docker topics ## RelativeMeasures.csv Relative measure of month-wise distribution of docker topics ## RelativeMeasures2.csv Source csv file used in ‘man_kendall.py’ file (relative measure of month-wise distribution of docker topics) ## DockerDataset.csv Due to space restrictions, only Id of docker related posts are provided. Id, title, body of docker posts can be found in this link: https://drive.google.com/open?id=1REUJvfQCua_lNCK4g5endPjbWydmk6Bw. Please cite: Mubin Ul Haque, Leonardo Horn Iwaya, and M. Ali Babar. 2020. Challenges in Docker Development: A Large-scale Study Using Stack Overflow. In ESEM ’20: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (ESEM ’20), October 8–9, 2020, Bari, Italy. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3382494.3410693