Statistical language modeling techniques have successfully been applied to large source
code corpora, yielding a variety of new software development tools, such as tools for code …

A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-
introducing. Recently, CC2Vec-a deep learning approach for Just-In-Time defect prediction …

Context: An excessive number of code smells make a software system hard to evolve and
maintain. Machine learning methods, in addition to metric-based and heuristic-based …

Software systems usually record important runtime information in their logs. Logs help
practitioners understand system runtime behaviors and diagnose field failures. As logs are …

Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …

Writing tests is a time-consuming yet essential task during software development. We
propose to leverage recent advances in deep learning for text and code generation to assist …

Defect prediction models are proposed to help a team prioritize the areas of source code
files that need Software Quality Assurance (SQA) based on the likelihood of having defects …

Code review is an effective quality assurance practice, but can be labor-intensive since
developers have to manually review the code and provide written feedback. Recently, a …

Designing practical security systems for the smart home is challenging without the
knowledge of realistic home usage. This paper describes the design and implementation of …

Code review is an effective quality assurance practice, yet can be time-consuming since
reviewers have to carefully review all new added lines in a patch. Our analysis shows that at …