  With software system complexity leading to the rise of software defects,
research efforts have been done on techniques towards predicting software
defects and Just-in-time (JIT) defect prediction which predicts whether a code
change is defective. While using features to determine potentially defective
code change, inspection effort is still significant. As code change can impact
several files, we investigate an open source project to identify potential gaps
with features in JIT perspective. In addition, with a lack of publicly
available JIT dataset that link the features with actual commits, we also
present a new dataset that can be utilized in JIT and semantic analysis.
