Sentiment analysis on java source code in large software repositories

Abstract

While developers are writing code to accomplish the task assigned to them, their sentiments play a vital role and have a massive impact on quality and productivity. Sentiments can have either a positive or a negative impact on the tasks being performed by developers. This thesis presents an analysis of developer commit logs for GitHub projects. In particular, developer sentiment in commits is analyzed across 28,466 projects within a seven-year time frame. We use the Boa infrastructure’s online query system to generate commit logs as well as files that were changed during the commit. Two existing sentiment analysis frameworks (SentiStrength and NLTK) are used for sentiment extraction. We analyze the commits in three categories: large, medium, and small based on the number of commits using sentiment analysis tools. In addition, we also group the data based on the day of week the commit was made and map the sentiment to the file change history to determine if there was any correlation. Although a majority of the sentiment was neutral, the negative sentiment was about 10% more than the positive sentiment overall. Tuesdays seem to have the most negative sentiment overall. In addition, we do find a strong correlation between the number of files changed and the sentiment expressed by the commits the files were part of. It was also observed that SentiStrength and NLTK show consistent results and similar trends. Future work and implications of these results are discussed.