RefBot: Intelligent Software Refactoring Bot
| Autor: | Vahid Alizadeh, Marouane Kessentini, Mohamed Amine Ouali, Meriem Chater |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 020207 software engineering 02 engineering and technology computer.software_genre Software quality Empirical research Code refactoring 0202 electrical engineering electronic engineering information engineering Software repository DevOps Software engineering business computer |
| Zdroj: | ASE |
| Popis: | The adoption of refactoring techniques for continuous integration received much less attention from the research community comparing to root-canal 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 pull-request. 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 bot's 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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