Improving Continuous Software Development in Academic Scenarios using GitHubTracker Continuous software development enables the early identification of potential problems in terms of software code, system integration, and team communication. In the field of software engineering education, usually, software development depends on the particular methodology for evaluating the technical performance of students in terms of software code and system integration of software development. Actually, GitHub is used for mining the information stored in GitHubu0027s event logs, trying to understand how its students to performance on software development. However, there are few studies and experiences describing how GitHub can support continuous software development. In this paper, we present a case study in two software engineering courses at the University of Talca using GitHubTracker. GitHubTracker is a tool for improving continuous software development using the main GitHubu0027s features. The results indicate that student teams were more effective in terms of performance and productivity than the other teams.

RefBot: Intelligent Software Refactoring Bot The adoption of refactoring techniques for continuous integration received much less attention from the research community comparing to rootcanal refactoring to fix the quality issues in the whole system. Several recent empirical studies show that developers, in practice, are applying refactoring incrementally when they are fixing bugs or adding new features. There is an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases. Furthermore, several studies show that manual refactoring is expensive and existing automated refactoring tools are challenging to configure and integrate into the development pipelines with significant disruption cost. In this paper, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Integrated into the version control system (e.g. GitHub), our bot continuously monitors the software repository, and it is triggered by any open or merge action on pull requests. The bot analyzes the files changed during that pull request to identify refactoring opportunities using a set of quality attributes then it will find the best sequence of refactorings to fix the quality issues if any. The bot recommends all these refactorings through an automatically generated pullrequest. The developer can review the recommendations and their impacts in a detailed report and select the code changes that he wants to keep or ignore. After this review, the developer can close and approve the merge of the botu0027s pull request. We quantitatively and qualitatively evaluated the performance and effectiveness of RefBot by a survey conducted with experienced developers who used the bot on both open source and industry projects