DocWarn

A replication package of "Towards Reliable Agile Iterative Planning via Predicting Documentation Changes of Work Items" published at the 19th International Conference on Mining Software Repositories – Technical track (MSR 2022 Technical track). Replication code DocWarn-T: /code/model/DocWarn_T.py DocWarn-H: /code/model/DocWarn_H.py DocWarn-C: /code/Rscript/DocWarn_C.R Analysis code for RQ1 can be found at /code/Rscript/ RQ1_performance_measure.R must be run first to measure the performance of the model. Then, run RQ1_performance_stattest.R to perform statistical test on the measured performance. RQ3_rank_features.R is used to find a statistical distinct rank for each features in DocWarn-C. Results and dataset can be found at /data (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) /data/data_reverted_cleaned stores dataset that the work items were reverted to sprint assignment time. /data/trainingData stores the dataset for each cross-validation round. /data/features stores the metrics extracted from each work items in the dataset. /data/modelResult stores the DocWarn-C R models (/models/...), performance of each DocWarn variations, and the result of features ranking. Manual classification /rq2_manual_validation.csv is the result of RQ2's manual classification to validate DocWarn-C. /rq2_manual_validation_external.csv is the manual classification that were done by the external coder (to measure the inter-rater agreement). code 0 = others, 1 = changing scope, 2 = defining scope, 3 = adding additional detail, 4 = adding implementation detail DistilRoberta for DocWarn-T and DocWarn-H can be found at /distilroberta-base-jira (only available on on Figshare version: https://figshare.com/s/88547b3c197b21b60f7c) This is the fine-tuned version of distilroberta-base with 110k JIRA issues.