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Software documentation captures detailed knowledge about a software product,e.g., code, technologies, and design. It plays an important role in thecoordination of development teams and in conveying ideas to variousstakeholders. However, software documentation can be hard to comprehend if itis written with jargon and complicated sentence structure. In this study, weexplored the potential of text simplification techniques in the domain ofsoftware engineering to automatically simplify GitHub README files. Wecollected software-related pairs of GitHub README files consisting of 14,588entries, aligned difficult sentences with their simplified counterparts, andtrained a Transformer-based model to automatically simplify difficult versions.To mitigate the sparse and noisy nature of the software-related simplificationdataset, we applied general text simplification knowledge to this field. Sincemany general-domain difficult-to-simple Wikipedia document pairs are alreadypublicly available, we explored the potential of transfer learning by firsttraining the model on the Wikipedia data and then fine-tuning it on the READMEdata. Using automated BLEU scores and human evaluation, we compared theperformance of different transfer learning schemes and the baseline modelswithout transfer learning. The transfer learning model using the bestcheckpoint trained on a general topic corpus achieved the best performance of34.68 BLEU score and statistically significantly higher human annotation scorescompared to the rest of the schemes and baselines. We conclude that usingtransfer learning is a promising direction to circumvent the lack of data anddrift style problem in software README files simplification and achieved abetter trade-off between simplification and preservation of meaning.

Evaluating Transfer Learning for Simplifying GitHub READMEs