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.

本资源为发表于第19届国际软件仓库挖掘会议技术专场（MSR 2022技术专场）的论文《面向通过预测工作项文档变更实现可靠敏捷迭代规划》的复现包。
本次复现所用代码如下：DocWarn-T 对应 /code/model/DocWarn_T.py，DocWarn-H 对应 /code/model/DocWarn_H.py，DocWarn-C 对应 /code/Rscript/DocWarn_C.R。
针对研究问题1（RQ1）的分析代码存放于/code/Rscript/目录下，需优先运行RQ1_performance_measure.R以计算模型性能，随后执行RQ1_performance_stattest.R对所得性能结果开展统计检验。RQ3_rank_features.R 用于为DocWarn-C中的每个特征提取具备统计学显著性的排序结果。
研究结果与数据集存储于/data目录（仅可在Figshare版本中获取：https://figshare.com/s/88547b3c197b21b60f7c），该目录下各子目录内容说明如下：
- /data/data_reverted_cleaned：存储了回溯至迭代冲刺分配阶段的工作项数据集；
- /data/trainingData：存储了每一轮交叉验证所用的训练数据集；
- /data/features：存储了从数据集中每个工作项提取得到的指标数据；
- /data/modelResult：存储了DocWarn-C的R模型（位于/models/目录下）、各DocWarn变体的性能结果以及特征排序结果。
/rq2_manual_validation.csv 为RQ2的手动分类结果，用于验证DocWarn-C模型；/rq2_manual_validation_external.csv 为外部编码人员完成的手动分类结果，用于衡量编码者间的一致性。
代码标签定义如下：0代表其他类别，1代表变更范围，2代表定义范围，3代表补充额外细节，4代表添加实现细节。
用于DocWarn-T与DocWarn-H的DistilRoberta模型存放于/distilroberta-base-jira目录下（仅可在Figshare版本中获取：https://figshare.com/s/88547b3c197b21b60f7c），该模型为基于11万个JIRA问题微调后的distilroberta-base版本。